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SocialGenPod: Privacy-Friendly Generative AI Social Web Applications with Decentralised Personal Data Stores arXiv.cs.SI Pub Date : 2024-03-15 Vidminas VizgirdaUniversity of Edinburgh, Rui ZhaoUniversity of Oxford, Naman GoelUniversity of Oxford
We present SocialGenPod, a decentralised and privacy-friendly way of deploying generative AI Web applications. Unlike centralised Web and data architectures that keep user data tied to application and service providers, we show how one can use Solid -- a decentralised Web specification -- to decouple user data from generative AI applications. We demonstrate SocialGenPod using a prototype that allows
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Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs arXiv.cs.SI Pub Date : 2024-03-15 Zhanke Zhou, Yongqi Zhang, Jiangchao Yao, Quanming Yao, Bo Han
To deduce new facts on a knowledge graph (KG), a link predictor learns from the graph structure and collects local evidence to find the answer to a given query. However, existing methods suffer from a severe scalability problem due to the utilization of the whole KG for prediction, which hinders their promise on large scale KGs and cannot be directly addressed by vanilla sampling methods. In this work
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Graph Enhanced Reinforcement Learning for Effective Group Formation in Collaborative Problem Solving arXiv.cs.SI Pub Date : 2024-03-15 Zheng Fang, Fucai Ke, Jae Young Han, Zhijie Feng, Toby Cai
This study addresses the challenge of forming effective groups in collaborative problem-solving environments. Recognizing the complexity of human interactions and the necessity for efficient collaboration, we propose a novel approach leveraging graph theory and reinforcement learning. Our methodology involves constructing a graph from a dataset where nodes represent participants, and edges signify
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From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News arXiv.cs.SI Pub Date : 2024-03-14 Yuhan Liu, Xiuying Chen, Xiaoqing Zhang, Xing Gao, Ji Zhang, Rui Yan
In the digital era, the rapid propagation of fake news and rumors via social networks brings notable societal challenges and impacts public opinion regulation. Traditional fake news modeling typically forecasts the general popularity trends of different groups or numerically represents opinions shift. However, these methods often oversimplify real-world complexities and overlook the rich semantic information
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Rumor Mitigation in Social Media Platforms with Deep Reinforcement Learning arXiv.cs.SI Pub Date : 2024-03-14 Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin, Yong Li
Social media platforms have become one of the main channels where people disseminate and acquire information, of which the reliability is severely threatened by rumors widespread in the network. Existing approaches such as suspending users or broadcasting real information to combat rumors are either with high cost or disturbing users. In this paper, we introduce a novel rumor mitigation paradigm, where
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The role of susceptible individuals in spreading dynamics arXiv.cs.SI Pub Date : 2024-03-13 Chang Su, Fang Zhou, Linyuan Lü
Exploring the internal mechanism of information spreading is critical for understanding and controlling the process. Traditional spreading models often assume individuals play the same role in the spreading process. In reality, however, individuals' diverse characteristics contribute differently to the spreading performance, leading to a heterogeneous infection rate across the system. To investigate
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Negative Impact of Online Political Incivility on Willingness to See Political Comments arXiv.cs.SI Pub Date : 2024-03-13 Kohei Nishi
Recently, there has been significant attention on online political incivility. While previous research suggests that uncivil political comments lead people to be less willing to see more comments on the same issue, two critical questions have received limited exploration: (1) Are people exposed to uncivil political comments less willing to see other comments from the person who posted the uncivil comment
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An improvement on the Louvain algorithm using random walks arXiv.cs.SI Pub Date : 2024-03-13 Duy Hieu Do, Thi Ha Duong Phan
We will present improvements to famous algorithms for community detection, namely Newman's spectral method algorithm and the Louvain algorithm. The Newman algorithm begins by treating the original graph as a single cluster, then repeats the process to split each cluster into two, based on the signs of the eigenvector corresponding to the secondlargest eigenvalue. Our improvement involves replacing
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Overlapping community detection algorithms using Modularity and the cosine arXiv.cs.SI Pub Date : 2024-03-12 Do Duy Hieu, Phan Thi Ha Duong
The issue of network community detection has been extensively studied across many fields. Most community detection methods assume that nodes belong to only one community. However, in many cases, nodes can belong to multiple communities simultaneously.This paper presents two overlapping network community detection algorithms that build on the two-step approach, using the extended modularity and cosine
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Online Misogyny Against Female Candidates in the 2022 Brazilian Elections: A Threat to Women's Political Representation? arXiv.cs.SI Pub Date : 2024-03-12 Luise Kocha, Raji Ghawi, Jürgen Pfeffer, Janina Isabel Steinert
Technology-facilitated gender-based violence has become a global threat to women's political representation and democracy. Understanding how online hate affects its targets is thus paramount. We analyse 10 million tweets directed at female candidates in the Brazilian election in 2022 and examine their reactions to online misogyny. Using a self-trained machine learning classifier to detect Portuguese
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Maximum Defective Clique Computation: Improved Time Complexities and Practical Performance arXiv.cs.SI Pub Date : 2024-03-12 Lijun Chang
The concept of $k$-defective clique, a relaxation of clique by allowing up-to $k$ missing edges, has been receiving increasing interests recently. Although the problem of finding the maximum $k$-defective clique is NP-hard, several practical algorithms have been recently proposed in the literature, with kDC being the state of the art. kDC not only runs the fastest in practice, but also achieves the
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Graph Data Condensation via Self-expressive Graph Structure Reconstruction arXiv.cs.SI Pub Date : 2024-03-12 Zhanyu Liu, Chaolv Zeng, Guanjie Zheng
With the increasing demands of training graph neural networks (GNNs) on large-scale graphs, graph data condensation has emerged as a critical technique to relieve the storage and time costs during the training phase. It aims to condense the original large-scale graph to a much smaller synthetic graph while preserving the essential information necessary for efficiently training a downstream GNN. However
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Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews arXiv.cs.SI Pub Date : 2024-03-11 Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, James Y. Zou
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI
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Breaking Political Filter Bubbles via Social Comparison arXiv.cs.SI Pub Date : 2024-03-11 Nouran Soliman, Motahhare Eslami, Karrie Karahalios
Online social platforms allow users to filter out content they do not like. According to selective exposure theory, people tend to view content they agree with more to get more self-assurance. This causes people to live in ideological filter bubbles. We report on a user study that encourages users to break the political filter bubble of their Twitter feed by reading more diverse viewpoints through
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Socio-spatial segregation and human mobility: A review of empirical evidence arXiv.cs.SI Pub Date : 2024-03-11 Yuan Liao, Jorge Gil, Sonia Yeh, Rafael H. M. Pereira, Laura Alessandretti
Social segregation, the spatial and social separation between individuals from different backgrounds, can affect sustainable urban development and social cohesion. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on income, ethnicity, and education. However, this approach overlooks
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RA-ICM: A Novel Independent Cascade Model Incorporating User Relationships and Attitudes arXiv.cs.SI Pub Date : 2024-03-11 Xinyu Li, Yutong Guo, Jixuan He, Jiacheng Zhao, Chenwei Wang
The rapid development of social networks has a wide range of social effects, which facilitates the study of social issues. Accurately forecasting the information propagation process within social networks is crucial for promptly understanding the event direction and effectively addressing social problems in a scientific manner. The relationships between non-adjacent users and the attitudes of users
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Information Dissemination Model Based on User Attitude and Public Opinion Environment arXiv.cs.SI Pub Date : 2024-03-10 Xinyu Li, Jinyang Huang, Xiang Zhang, Peng Zhao, Meng Wang, Guohang Zhuang, Huan Yan, Xiao Sun, Meng Wang
Modeling the information dissemination process in social networks is a challenging problem. Despite numerous attempts to address this issue, existing studies often assume that user attitudes have only one opportunity to alter during the information dissemination process. Additionally, these studies tend to consider the transformation of user attitudes as solely influenced by a single user, overlooking
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Redefining Event Types and Group Evolution in Temporal Data arXiv.cs.SI Pub Date : 2024-03-11 Andrea Failla, Rémy Cazabet, Giulio Rossetti, Salvatore Citraro
Groups -- such as clusters of points or communities of nodes -- are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of ``events". However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such
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Deciphering Crypto Twitter arXiv.cs.SI Pub Date : 2024-03-09 Inwon Kang, Maruf Ahmed Mridul, Abraham Sanders, Yao Ma, Thilanka Munasinghe, Aparna Gupta, Oshani Seneviratne
Cryptocurrency is a fast-moving space, with a continuous influx of new projects every year. However, an increasing number of incidents in the space, such as hacks and security breaches, threaten the growth of the community and the development of technology. This dynamic and often tumultuous landscape is vividly mirrored and shaped by discussions within Crypto Twitter, a key digital arena where investors
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Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks arXiv.cs.SI Pub Date : 2024-03-08 Yiwei Zou, Ting Li, Zong-fu Luo
Closeness Centrality (CC) and Betweenness Centrality (BC) are crucial metrics in network analysis, providing essential reference for discerning the significance of nodes within complex networks. These measures find wide applications in critical tasks, such as community detection and network dismantling. However, their practical implementation on extensive networks remains computationally demanding
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Corrective or Backfire: Characterizing and Predicting User Response to Social Correction arXiv.cs.SI Pub Date : 2024-03-07 Bing He, Yingchen Ma, Mustaque Ahamad, Srijan Kumar
Online misinformation poses a global risk with harmful implications for society. Ordinary social media users are known to actively reply to misinformation posts with counter-misinformation messages, which is shown to be effective in containing the spread of misinformation. Such a practice is defined as "social correction". Nevertheless, it remains unknown how users respond to social correction in real-world
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SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media arXiv.cs.SI Pub Date : 2024-03-08 Parisa Jamadi Khiabani, Arkaitz Zubiaga
Stance detection, as the task of determining the viewpoint of a social media post towards a target as 'favor' or 'against', has been understudied in the challenging yet realistic scenario where there is limited labeled data for a certain target. Our work advances research in few-shot stance detection by introducing SocialPET, a socially informed approach to leveraging language models for the task.
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Automating the Information Extraction from Semi-Structured Interview Transcripts arXiv.cs.SI Pub Date : 2024-03-07 Angelina Parfenova
This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. Given the labor-intensive nature of traditional qualitative analysis methods, such as coding, there exists a significant demand for tools that can facilitate the analysis process. Our research investigates various topic modeling techniques and concludes
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Understanding how social discussion platforms like Reddit are influencing financial behavior arXiv.cs.SI Pub Date : 2024-03-07 Sachin Thukral, Suyash Sangwan, Arnab Chatterjee, Lipika Dey, Aaditya Agrawal, Pramit Kumar Chandra, Animesh Mukherjee
This study proposes content and interaction analysis techniques for a large repository created from social media content. Though we have presented our study for a large platform dedicated to discussions around financial topics, the proposed methods are generic and applicable to all platforms. Along with an extension of topic extraction method using Latent Dirichlet Allocation, we propose a few measures
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Generating insights about financial asks from Reddit posts and user interactions arXiv.cs.SI Pub Date : 2024-03-07 Sachin Thukral, Suyash Sangwan, Vipul Chauhan, Arnab Chatterjee, Lipika Dey
As an increasingly large number of people turn to platforms like Reddit, YouTube, Twitter, Instagram, etc. for financial advice, generating insights about the content generated and interactions taking place within these platforms have become a key research question. This study proposes content and interaction analysis techniques for a large repository created from social media content, where people
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Improving link prediction accuracy of network embedding algorithms via rich node attribute information arXiv.cs.SI Pub Date : 2024-03-07 Weiwei Gu, Jinqiang Hou, Weiyi Gu
Complex networks are widely used to represent an abundance of real-world relations ranging from social networks to brain networks. Inferring missing links or predicting future ones based on the currently observed network is known as the link prediction task.Recent network embedding based link prediction algorithms have demonstrated ground-breaking performance on link prediction accuracy. Those algorithms
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Exploring the Impact of Opinion Polarization on Short Video Consumption arXiv.cs.SI Pub Date : 2024-03-07 Bangde Du, Ziyi Ye, Zhijing Wu, Qingyao Ai, Yiqun Liu
Investigating the increasingly popular domain of short video consumption, this study focuses on the impact of Opinion Polarization (OP), a significant factor in the digital landscape influencing public opinions and social interactions. We analyze OP's effect on viewers' perceptions and behaviors, finding that traditional feedback metrics like likes and watch time fail to fully capture and measure OP
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Exploratory Factory Analysis of the Centrality Metrics for Complex Real-World Networks arXiv.cs.SI Pub Date : 2024-03-06 Natarajan Meghanathan
Exploratory factor analysis (EFA) is useful to identify the number and mapping of the hidden factors that could dominantly represent the features in the dataset. Principal component analysis (PCA) is the first step as part of the two-step procedure to conduct EFA, with the number of dominant principal components being the number of hidden factors and the entries for the features in the corresponding
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Political polarisation in turbulent times: Tracking polarisation trends and partisan news link sharing on Finnish Twitter, 2015-2023 arXiv.cs.SI Pub Date : 2024-03-06 Antti Gronow, Arttu Malkamäki
The study analyses polarisation on Finnish social media with data from the platform X, which was known as Twitter during the time of data collection (during the Sipil\"a and Marin governments, 2015-2023). The users were clustered into three different ideological groups - the Conservative Right, the Moderate Right, and the Liberal Left - based on their retweeting of tweets referring to the different
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Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation arXiv.cs.SI Pub Date : 2024-03-06 Li Wang, Min Xu, Quangui Zhang, Yunxiao Shi, Qiang Wu
Social recommendation systems face the problem of social influence bias, which can lead to an overemphasis on recommending items that friends have interacted with. Addressing this problem is crucial, and existing methods often rely on techniques such as weight adjustment or leveraging unbiased data to eliminate this bias. However, we argue that not all biases are detrimental, i.e., some items recommended
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Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding arXiv.cs.SI Pub Date : 2024-03-06 Mengying Jiang, Guizhong Liu, Yuanchao Su, Xinliang Wu
In representation learning on graph-structured data, many popular graph neural networks (GNNs) fail to capture long-range dependencies, leading to performance degradation. Furthermore, this weakness is magnified when the concerned graph is characterized by heterophily (low homophily). To solve this issue, this paper proposes a novel graph learning framework called the graph convolutional network with
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Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse arXiv.cs.SI Pub Date : 2024-03-05 Joseph Gatto, Madhusudan Basak, Yash Srivastava, Philip Bohlman, Sarah M. Preum
In this paper, we develop an LLM-powered framework for the curation and evaluation of emerging opinion mining in online health communities. We formulate emerging opinion mining as a pairwise stance detection problem between (title, comment) pairs sourced from Reddit, where post titles contain emerging health-related claims on a topic that is not predefined. The claims are either explicitly or implicitly
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Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation arXiv.cs.SI Pub Date : 2024-03-05 Keke Huang, Ruize Gao, Bogdan Cautis, Xiaokui Xiao
The study of continuous-time information diffusion has been an important area of research for many applications in recent years. When only the diffusion traces (cascades) are accessible, cascade-based network inference and influence estimation are two essential problems to explore. Alas, existing methods exhibit limited capability to infer and process networks with more than a few thousand nodes, suffering
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Backfire Effect Reveals Early Controversy in Online Media arXiv.cs.SI Pub Date : 2024-03-05 Songtao Peng, Chenbo Fua, Han Han, Ye Wu, Kailun Zhu, Qi Xuan, Yong Min
The rapid development of online media has significantly facilitated the public's information consumption, knowledge acquisition, and opinion exchange. However, it has also led to more violent conflicts in online discussions. Therefore, controversy detection becomes important for computational and social sciences. Previous research on detection methods has primarily focused on larger datasets and more
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FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling arXiv.cs.SI Pub Date : 2024-03-05 Hongyu Zhang, Dongyi Zheng, Lin Zhong, Xu Yang, Jiyuan Feng, Yunqing Feng, Qing Liao
In recent years, Cross-Domain Recommendation (CDR) has drawn significant attention, which utilizes user data from multiple domains to enhance the recommendation performance. However, current CDR methods require sharing user data across domains, thereby violating the General Data Protection Regulation (GDPR). Consequently, numerous approaches have been proposed for Federated Cross-Domain Recommendation
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TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts arXiv.cs.SI Pub Date : 2024-03-05 Hyunwook Lee, Sungahn Ko
Accurate traffic forecasting is challenging due to the complex dependency on road networks, various types of roads, and the abrupt speed change due to the events. Recent works mainly focus on dynamic spatial modeling with adaptive graph embedding or graph attention having less consideration for temporal characteristics and in-situ modeling. In this paper, we propose a novel deep learning model named
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Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models arXiv.cs.SI Pub Date : 2024-03-03 Iakovos Evdaimon, Giannis Nikolentzos, Michail Chatzianastasis, Hadi Abdine, Michalis Vazirgiannis
Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties. Existing methods often fall short in efficiently addressing this need as they struggle with the high-dimensional complexity and varied nature of graph properties. In this paper, we introduce the Neural Graph Generator (NGG), a novel approach
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RCoCo: Contrastive Collective Link Prediction across Multiplex Network in Riemannian Space arXiv.cs.SI Pub Date : 2024-03-04 Li Sun, Mengjie Li, Yong Yang, Xiao Li, Lin Liu, Pengfei Zhang, Haohua Du
Link prediction typically studies the probability of future interconnection among nodes with the observation in a single social network. More often than not, real scenario is presented as a multiplex network with common (anchor) users active in multiple social networks. In the literature, most existing works study either the intra-link prediction in a single network or inter-link prediction among networks
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TweetInfo: An Interactive System to Mitigate Online Harm arXiv.cs.SI Pub Date : 2024-03-03 Gautam Kishore Shahi
The increase in active users on social networking sites (SNSs) has also observed an increase in harmful content on social media sites. Harmful content is described as an inappropriate activity to harm or deceive an individual or a group of users. Alongside existing methods to detect misinformation and hate speech, users still need to be well-informed about the harmfulness of the content on SNSs. This
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The Repercussions of the COVID-19 Pandemic on Higher Education and its implications for Syrian Refugees Students (An Analytical Descriptive Study) arXiv.cs.SI Pub Date : 2024-03-02 Anas Alsobeh, Ahlam Aloudat
This study aims to reveal the most important challenges and difficulties that refugee students faced in Jordanian universities (e.g., Yarmouk University, AL Al-Bayt, and the Private Zarqa University) due to the COVID-19 pandemic through measuring a different of indicators that are related, in addition, to identify some of the independent variables on e-educational challenges. In the study, the analytical
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The Effectiveness of a Training Program Based on Health Education to Improve Health Empowerment Level among Refugees in Jordan arXiv.cs.SI Pub Date : 2024-03-02 Ahmed AlSharifin, Muayyad Megdadi, Amani Shatnawi, Anas AlSobeh, Aya Akkawi
Objectives: The study aimed to evaluate the effectiveness of a health education-based training program in enhancing the level of health empowerment among refugees in Jordan. Health empowerment is a key component to promote health as it enables individuals to control and manage their health outcomes and improve them. Refugees are a vulnerable population group with limited access to healthcare. Methodology:
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Representation Learning on Heterophilic Graph with Directional Neighborhood Attention arXiv.cs.SI Pub Date : 2024-03-03 Qincheng Lu, Jiaqi Zhu, Sitao Luan, Xiao-Wen Chang
Graph Attention Network (GAT) is one of the most popular Graph Neural Network (GNN) architecture, which employs the attention mechanism to learn edge weights and has demonstrated promising performance in various applications. However, since it only incorporates information from immediate neighborhood, it lacks the ability to capture long-range and global graph information, leading to unsatisfactory
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GSL-LPA: Fast Label Propagation Algorithm (LPA) for Community Detection with no Internally-Disconnected Communities arXiv.cs.SI Pub Date : 2024-03-02 Subhajit Sahu
Community detection is the problem of identifying tightly connected clusters of nodes within a network. Efficient parallel algorithms for this play a crucial role in various applications, especially as datasets expand to significant sizes. The Label Propagation Algorithm (LPA) is commonly employed for this purpose due to its ease of parallelization, rapid execution, and scalability. However, it may
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OpenGraph: Towards Open Graph Foundation Models arXiv.cs.SI Pub Date : 2024-03-02 Lianghao Xia, Ben Kao, Chao Huang
Graph learning has become indispensable for interpreting and harnessing relational data in diverse fields, ranging from recommendation systems to social network analysis. In this context, a variety of GNNs have emerged as promising methodologies for encoding the structural information of graphs. By effectively capturing the graph's underlying structure, these GNNs have shown great potential in enhancing
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COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting arXiv.cs.SI Pub Date : 2024-03-02 Wei Ju, Yusheng Zhao, Yifang Qin, Siyu Yi, Jingyang Yuan, Zhiping Xiao, Xiao Luo, Xiting Yan, Ming Zhang
This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in various scenarios and facilitated the development of numerous downstream applications such as urban planning and transportation management. However, the efficacy of existing methods remains sub-optimal due to their
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Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language arXiv.cs.SI Pub Date : 2024-03-01 Xiaohan Ding, Buse Carik, Uma Sushmitha Gunturi, Valerie Reyna, Eugenia H. Rho
We introduce a multi-step reasoning framework using prompt-based LLMs to examine the relationship between social media language patterns and trends in national health outcomes. Grounded in fuzzy-trace theory, which emphasizes the importance of gists of causal coherence in effective health communication, we introduce Role-Based Incremental Coaching (RBIC), a prompt-based LLM framework, to identify gists
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Team Formation amidst Conflicts arXiv.cs.SI Pub Date : 2024-02-29 Iasonas Nikolaou, Evimaria Terzi
In this work, we formulate the problem of team formation amidst conflicts. The goal is to assign individuals to tasks, with given capacities, taking into account individuals' task preferences and the conflicts between them. Using dependent rounding schemes as our main toolbox, we provide efficient approximation algorithms. Our framework is extremely versatile and can model many different real-world
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Cost-Effective Activity Control of Asymptomatic Carriers in Layered Temporal Social Networks arXiv.cs.SI Pub Date : 2024-03-01 Masoumeh Moradian, Aresh Dadlani, Rasul Kairgeldin, Ahmad Khonsari
The robustness of human social networks against epidemic propagation relies on the propensity for physical contact adaptation. During the early phase of infection, asymptomatic carriers exhibit the same activity level as susceptible individuals, which presents challenges for incorporating control measures in epidemic projection models. This paper focuses on modeling and cost-efficient activity control
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Shorts vs. Regular Videos on YouTube: A Comparative Analysis of User Engagement and Content Creation Trends arXiv.cs.SI Pub Date : 2024-03-01 Caroline ViolotUniversity of Lausanne, Tuğrulcan ElmasIndiana University Bloomington, Igor BilogrevicGoogle, Mathias HumbertUniversity of Lausanne
YouTube introduced the Shorts video format in 2021, allowing users to upload short videos that are prominently displayed on its website and app. Despite having such a large visual footprint, there are no studies to date that have looked at the impact Shorts introduction had on the production and consumption of content on YouTube. This paper presents the first comparative analysis of YouTube Shorts
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Identification of important nodes in the information propagation network based on the artificial intelligence method arXiv.cs.SI Pub Date : 2024-02-29 Bin Yuan, Tianbo Song, Jerry Yao
This study presents an integrated approach for identifying key nodes in information propagation networks using advanced artificial intelligence methods. We introduce a novel technique that combines the Decision-making Trial and Evaluation Laboratory (DEMATEL) method with the Global Structure Model (GSM), creating a synergistic model that effectively captures both local and global influences within
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"There is a Job Prepared for Me Here": Understanding How Short Video and Live-streaming Platforms Empower Ageing Job Seekers in China arXiv.cs.SI Pub Date : 2024-03-01 PiaoHong Wang, Siying Hu, Bo Wen, Zhicong Lu
In recent years, the global unemployment rate has remained persistently high. Compounding this issue, the ageing population in China often encounters additional challenges in finding employment due to prevalent age discrimination in daily life. However, with the advent of social media, there has been a rise in the popularity of short videos and live-streams for recruiting ageing workers. To better
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Higher-Order Networks Representation and Learning: A Survey arXiv.cs.SI Pub Date : 2024-02-29 Hao Tian, Reza Zafarani
Network data has become widespread, larger, and more complex over the years. Traditional network data is dyadic, capturing the relations among pairs of entities. With the need to model interactions among more than two entities, significant research has focused on higher-order networks and ways to represent, analyze, and learn from them. There are two main directions to studying higher-order networks
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Link Recommendation to Augment Influence Diffusion with Provable Guarantees arXiv.cs.SI Pub Date : 2024-02-29 Xiaolong Chen, Yifan Song, Jing Tang
Link recommendation systems in online social networks (OSNs), such as Facebook's ``People You May Know'', Twitter's ``Who to Follow'', and Instagram's ``Suggested Accounts'', facilitate the formation of new connections among users. This paper addresses the challenge of link recommendation for the purpose of social influence maximization. In particular, given a graph $G$ and the seed set $S$, our objective
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Online disinformation in the 2020 U.S. Election: swing vs. safe states arXiv.cs.SI Pub Date : 2024-02-28 Manuel Pratelli, Marinella Petrocchi, Fabio Saracco, Rocco De Nicola
For U.S. presidential elections, most states use the so-called winner-take-all system, in which the state's presidential electors are awarded to the winning political party in the state after a popular vote phase, regardless of the actual margin of victory. Therefore, election campaigns are especially intense in states where there is no clear direction on which party will be the winning party. These
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Unveiling News Publishers Trustworthiness Through Social Interactions arXiv.cs.SI Pub Date : 2024-02-28 Manuel Pratelli, Fabio Saracco, Marinella Petrocchi
With the primary goal of raising readers' awareness of misinformation phenomena, extensive efforts have been made by both academic institutions and independent organizations to develop methodologies for assessing the trustworthiness of online news publishers. Unfortunately, existing approaches are costly and face critical scalability challenges. This study presents a novel framework for assessing the
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#PoetsOfInstagram: Navigating The Practices And Challenges Of Novice Poets On Instagram arXiv.cs.SI Pub Date : 2024-02-29 Ankolika De, Zhicong Lu
Commencing as a photo-sharing platform, Instagram has since become multifaceted, accommodating diverse art forms, with poetry emerging as a prominent one. However, the academic understanding of Instagram's poetry community is limited, yet its significance emerges from its distinctive utilization of a primarily visual social media platform guided by recommendation algorithms for disseminating poetry
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Seeking Soulmate via Voice: Understanding Promises and Challenges of Online Synchronized Voice-Based Mobile Dating arXiv.cs.SI Pub Date : 2024-02-29 Chenxinran Shen, Yan Xu, Ray LC, Zhicong Lu
Online dating has become a popular way for individuals to connect with potential romantic partners. Many dating apps use personal profiles that include a headshot and self-description, allowing users to present themselves and search for compatible matches. However, this traditional model often has limitations. In this study, we explore a non-traditional voice-based dating app called "Soul". Unlike
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Like-minded, like-bodied: How users (18-26) trust online eating and health information arXiv.cs.SI Pub Date : 2024-02-28 Rachel Xu, Nhu Le, Rebekah Park, Laura Murray
This paper investigates the relationship between social media and eating practices amongst 42 internet users aged 18-26. We conducted an ethnography in the US and India to observe how they navigated eating and health information online. We found that participants portrayed themselves online through a vocabulary we have labeled "the good life": performing holistic health by displaying a socially-ideal
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Detecting Anti-vaccine Content on Twitter using Multiple Message-Based Network Representations arXiv.cs.SI Pub Date : 2024-02-28 James R. Ashford
Social media platforms such as Twitter have a fundamental role in facilitating the spread and discussion of ideas online through the concept of retweeting and replying. However, these features also contribute to the spread of mis/disinformation during the vaccine rollout of the COVID-19 pandemic. Using COVID-19 vaccines as a case study, we analyse multiple social network representation derived from
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On the Joint Effect of Culture and Discussion Topics on X (Twitter) Signed Ego Networks arXiv.cs.SI Pub Date : 2024-02-28 Jack Tacchi, Chiara Boldrini, Andrea Passarella, Marco Conti
Humans are known to structure social relationships according to certain patterns, such as the Ego Network Model (ENM). These patterns result from our innate cognitive limits and can therefore be observed in the vast majority of large human social groups. Until recently, the main focus of research was the structural characteristics of this model. The main aim of this paper is to complement previous