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  • Estimating network memberships by mixed regularized spectral clustering
    arXiv.cs.SI Pub Date : 2020-11-23
    Huan Qing; Jingli Wang

    Mixed membership community detection is a challenge problem in network analysis. Here, under the degree-corrected mixed membership (DCMM) model, we propose an efficient approach called mixed regularized spectral clustering (Mixed-RSC for short) to estimate the memberships. Mixed-RSC is an extension of the RSC method (Qin and Rohe, 2013) to deal with the mixed membership community detection problem

    更新日期:2020-11-25
  • xFraud: Explainable Fraud Transaction Detection on Heterogeneous Graphs
    arXiv.cs.SI Pub Date : 2020-11-24
    Susie Xi Rao; Shuai Zhang; Zhichao Han; Zitao Zhang; Wei Min; Zhiyao Chen; Yinan Shan; Yang Zhao; Ce Zhang

    At online retail platforms, it is crucial to actively detect risks of fraudulent transactions to improve our customer experience, minimize loss, and prevent unauthorized chargebacks. Traditional rule-based methods and simple feature-based models are either inefficient or brittle and uninterpretable. The graph structure that exists among the heterogeneous typed entities of the transaction logs is informative

    更新日期:2020-11-25
  • Revisit graph neural networks and distance encoding in a practical view
    arXiv.cs.SI Pub Date : 2020-11-22
    Haoteng Yin; Yanbang Wang; Pan Li

    Graph neural networks (GNNs) are widely used in the applications based on graph structured data, such as node classification and link prediction. However, GNNs are often used as a black-box tool and rarely get in-depth investigated regarding whether they fit certain applications that may have various properties. A recently proposed technique distance encoding (DE) (Li et al. 2020) magically makes GNNs

    更新日期:2020-11-25
  • Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction
    arXiv.cs.SI Pub Date : 2020-11-24
    Mariam Alaverdian; William Gilroy; Veronica Kirgios; Xia Li; Carolina Matuk; Daniel Mckenzie; Tachin Ruangkriengsin; Andrea Bertozzi; Jeffrey Brantingham

    We present a preliminary study of a knowledge graph created from season one of the television show Veronica Mars, which follows the eponymous young private investigator as she attempts to solve the murder of her best friend Lilly Kane. We discuss various techniques for mining the knowledge graph for clues and potential suspects. We also discuss best practice for collaboratively constructing knowledge

    更新日期:2020-11-25
  • Tracking the evolution of crisis processes and mental health on social media during the COVID-19 pandemic
    arXiv.cs.SI Pub Date : 2020-11-22
    Antonela Tommasel; Daniela Godoy; Juan Manuel Rodriguez

    The COVID-19 pandemic has affected all aspects of society, not only bringing health hazards, but also posing challenges to public order, governments and mental health. Moreover, it is the first one in history in which people from around the world uses social media to massively express their thoughts and concerns. This study aims at examining the stages of crisis response and recovery as a sociological

    更新日期:2020-11-25
  • Mining Influentials and their Bot Activities on Twitter Campaigns
    arXiv.cs.SI Pub Date : 2020-11-22
    Shanika Karunasekera; Kwan Hui Lim; Aaron Harwood

    Twitter is increasingly used for political, advertising and marketing campaigns, where the main aim is to influence users to support specific causes, individuals or groups. We propose a novel methodology for mining and analyzing Twitter campaigns, which includes: (i) collecting tweets and detecting topics relating to a campaign; (ii) mining important campaign topics using scientometrics measures; (iii)

    更新日期:2020-11-25
  • Detecting Fake News Spreaders in Social Networks using Inductive Representation Learning
    arXiv.cs.SI Pub Date : 2020-11-21
    Bhavtosh Rath; Aadesh Salecha; Jaideep Srivastava

    An important aspect of preventing fake news dissemination is to proactively detect the likelihood of its spreading. Research in the domain of fake news spreader detection has not been explored much from a network analysis perspective. In this paper, we propose a graph neural network based approach to identify nodes that are likely to become spreaders of false information. Using the community health

    更新日期:2020-11-25
  • Measuring Quadrangle Formation in Complex Networks
    arXiv.cs.SI Pub Date : 2020-11-21
    Mingshan Jia; Bogdan Gabrys; Katarzyna Musial

    The classic clustering coefficient and the lately proposed closure coefficient quantify the formation of triangles from two different perspectives, with the focal node at the centre or at the end in an open triad respectively. As many networks are naturally rich in triangles, they become standard metrics to describe and analyse networks. However, the advantages of applying them can be limited in networks

    更新日期:2020-11-25
  • APAN: Asynchronous Propagate Attention Network for Real-time Temporal Graph Embedding
    arXiv.cs.SI Pub Date : 2020-11-23
    Xuhong Wang; Ding Lyu; Mengjian Li; Yang Xia; Qi Yang; Xinwen Wang; Xinguang Wang; Ping Cui; Yupu Yang; Bowen Sun; Zhenyu Guo

    Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference. This problem dramatically limits the potential of applying graph algorithms in certain areas, such as financial fraud detection. Therefore, we propose Asynchronous Propagate Attention Network, an asynchronous continuous time dynamic

    更新日期:2020-11-25
  • AutoGraph: Automated Graph Neural Network
    arXiv.cs.SI Pub Date : 2020-11-23
    Yaoman Li; Irwin King

    Graphs play an important role in many applications. Recently, Graph Neural Networks (GNNs) have achieved promising results in graph analysis tasks. Some state-of-the-art GNN models have been proposed, e.g., Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), etc. Despite these successes, most of the GNNs only have shallow structure. This causes the low expressive power of the GNNs

    更新日期:2020-11-25
  • Edge Deletion Algorithms for Minimizing Spread in SIR Epidemic Models
    arXiv.cs.SI Pub Date : 2020-11-22
    Yuhao Yi; Liren Shan; Philip E. Paré; Karl H. Johansson

    This paper studies algorithmic strategies to effectively reduce the number of infections in susceptible-infected-recovered (SIR) epidemic models. We consider a Markov chain SIR model and its two instantiations in the deterministic SIR (D-SIR) model and the independent cascade SIR (IC-SIR) model. We investigate the problem of minimizing the number of infections by restricting contacts under realistic

    更新日期:2020-11-25
  • GNNVis: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
    arXiv.cs.SI Pub Date : 2020-11-22
    Zhihua Jin; Yong Wang; Qianwen Wang; Yao Ming; Tengfei Ma; Huamin Qu

    Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users

    更新日期:2020-11-25
  • A General Framework for Distributed Inference with Uncertain Models
    arXiv.cs.SI Pub Date : 2020-11-20
    James Z. Hare; Cesar A. Uribe; Lance Kaplan; Ali Jadbabaie

    This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesis-testing framework, where we assume that the agents seek to agree on the hypothesis (target class) that best matches the distribution of observations

    更新日期:2020-11-25
  • Exploring the political pulse of a country using data science tools
    arXiv.cs.SI Pub Date : 2020-11-20
    Miguel G. Folgado; Verónica Sanz

    In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted

    更新日期:2020-11-23
  • Graph Signal Recovery Using Restricted Boltzmann Machines
    arXiv.cs.SI Pub Date : 2020-11-20
    Ankith Mohan; Aiichiro Nakano; Emilio Ferrara

    We propose a model-agnostic pipeline to recover graph signals from an expert system by exploiting the content addressable memory property of restricted Boltzmann machine and the representational ability of a neural network. The proposed pipeline requires the deep neural network that is trained on a downward machine learning task with clean data, data which is free from any form of corruption or incompletion

    更新日期:2020-11-23
  • Time-Series Snapshot Network as A New Model for Role Recommendation in OSS
    arXiv.cs.SI Pub Date : 2020-11-18
    Jinyin Chen; Yunyi Xie; Jian Zhang; Xincheng Shu; Qi Xuan

    The last decade has witnessed the rapid growth of open source software~(OSS). Still, all contributors may find it difficult to assimilate into OSS community even they are enthusiastic to make contributions. We thus suggest that role recommendation may benefit both the users and developers, i.e., once we are able to make successful role recommendation for those in need, it may dramatically contribute

    更新日期:2020-11-21
  • Graph embeddings via matrix factorization for link prediction: smoothing or truncating negatives?
    arXiv.cs.SI Pub Date : 2020-11-16
    Asan Agibetov

    Link prediction -- the process of uncovering missing links in a complex network -- is an important problem in information sciences, with applications ranging from social sciences to molecular biology. Recent advances in neural graph embeddings have proposed an end-to-end way of learning latent vector representations of nodes, with successful application in link prediction tasks. Yet, our understanding

    更新日期:2020-11-21
  • A Distributed Privacy-Preserving Learning Dynamics in General Social Networks
    arXiv.cs.SI Pub Date : 2020-11-15
    Youming Tao; Shuzhen Chen; Feng Li; Dongxiao Yu; Jiguo Yu; Hao Sheng

    In this paper, we study a distributed privacy-preserving learning problem in general social networks. Specifically, we consider a very general problem setting where the agents in a given multi-hop social network are required to make sequential decisions to choose among a set of options featured by unknown stochastic quality signals. Each agent is allowed to interact with its peers through multi-hop

    更新日期:2020-11-21
  • Contextual Stochastic Block Model: Sharp Thresholds and Contiguity
    arXiv.cs.SI Pub Date : 2020-11-15
    Chen Lu; Subhabrata Sen

    We study community detection in the contextual stochastic block model arXiv:1807.09596 [cs.SI], arXiv:1607.02675 [stat.ME]. In arXiv:1807.09596 [cs.SI], the second author studied this problem in the setting of sparse graphs with high-dimensional node-covariates. Using the non-rigorous cavity method from statistical physics, they conjectured the sharp limits for community detection in this setting.

    更新日期:2020-11-21
  • Towards mmWave V2X in 5G and Beyond to Support Automated Driving
    arXiv.cs.SI Pub Date : 2020-11-19
    Kei Sakaguchi; Ryuichi Fukatsu; Tao Yu; Eisuke Fukuda; Kim Mahler; Robert Heath; Takeo Fujii; Kazuaki Takahashi; Alexey Khoryaev; Satoshi Nagata; Takayuki Shimizu

    Millimeter wave provides high data rates for Vehicle-to-Everything (V2X) communications. This paper motivates millimeter wave to support automated driving and begins by explaining V2X use cases that support automated driving with references to several standardi-zation bodies. The paper gives a classification of existing V2X stand-ards: IEEE802.11p and LTE V2X, along with the status of their com-mercial

    更新日期:2020-11-21
  • A Deterministic Hitting-Time Moment Approach to Seed-set Expansion over a Graph
    arXiv.cs.SI Pub Date : 2020-11-18
    Alexander H. Foss; Richard B. Lehoucq; W. Zachary Stuart; J. Derek Tucker; Jonathan W. Berry

    We introduce HITMIX, a new technique for network seed-set expansion, i.e., the problem of identifying a set of graph vertices related to a given seed-set of vertices. We use the moments of the graph's hitting-time distribution to quantify the relationship of each non-seed vertex to the seed-set. This involves a deterministic calculation for the hitting-time moments that is scalable in the number of

    更新日期:2020-11-21
  • Evolution of the political opinion landscape during electoral periods
    arXiv.cs.SI Pub Date : 2020-11-18
    Tomás Mussi Reyero; Mariano G. Beiró; J. Ignacio Alvarez-Hamelin; Laura Hernández; Dimitris Kotzinos

    We present a study of the evolution of the political landscape during the 2015 and 2019 presidential elections in Argentina, based on the data obtained from the micro-blogging platform Twitter. We build a semantic network based on the hashtags used by all the users following at least one of the main candidates. With this network we can detect the topics that are discussed in the society. At a difference

    更新日期:2020-11-21
  • An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis and Recommendation
    arXiv.cs.SI Pub Date : 2020-11-19
    Soumyadeep Roy; Shamik Sural; Niyati Chhaya; Anandhavelu Natarajan; Niloy Ganguly

    A consumer-dependent (business-to-consumer) organization tends to present itself as possessing a set of human qualities, which is termed as the brand personality of the company. The perception is impressed upon the consumer through the content, be it in the form of advertisement, blogs or magazines, produced by the organization. A consistent brand will generate trust and retain customers over time

    更新日期:2020-11-21
  • Finding Your Way: Shortest Paths on Networks
    arXiv.cs.SI Pub Date : 2020-11-19
    Teresa Rexin; Mason A. Porter

    Traveling to different destinations is a big part of our lives. How do we know the best way to navigate from one place to another? Perhaps we could test all of the different ways of traveling between two places, but another method is using mathematics and computation to find a shortest path. We discuss how to find a shortest path and introduce Dijkstra's algorithm to minimize the total cost of a path

    更新日期:2020-11-21
  • Connected-Dense-Connected Subgraphs in Triple Networks
    arXiv.cs.SI Pub Date : 2020-11-18
    Dhara Shah; Yubao Wu; Sushil Prasad; Danial Aghajarian

    Finding meaningful communities - subnetworks of interest within a large scale network - is a problem with a variety of applications. Most existing work towards community detection focuses on a single network. However, many real-life applications naturally yield what we refer to as Triple Networks. Triple Networks are comprised of two networks, and the network of bipartite connections between their

    更新日期:2020-11-19
  • Analysis of Cryptocurrency Transactions from a Network Perspective: An Overview
    arXiv.cs.SI Pub Date : 2020-11-18
    Jiajing Wu; Jieli Liu; Yijing Zhao; Zibin Zheng

    As one of the most important and famous applications of blockchain technology, cryptocurrency has attracted extensive attention recently. Empowered by blockchain technology, all the transaction records of cryptocurrencies are irreversible and recorded in the blocks. These transaction records containing rich information and complete traces of financial activities are publicly accessible, thus providing

    更新日期:2020-11-19
  • A First Look at COVID-19 Messages on WhatsAppin Pakistan
    arXiv.cs.SI Pub Date : 2020-11-18
    R. Tallal Javed; Mirza Elaaf Shuja; Muhammad Usama; Junaid Qadir; Waleed Iqbal; Gareth Tyson; Ignacio Castro; Kiran Garimella

    The worldwide spread of COVID-19 has prompted extensive online discussions, creating an `infodemic' on social media platforms such as WhatsApp and Twitter. However, the information shared on these platforms is prone to be unreliable and/or misleading. In this paper, we present the first analysis of COVID-19 discourse on public WhatsApp groups from Pakistan. Building on a large scale annotation of thousands

    更新日期:2020-11-19
  • Link prediction in multiplex networks via triadic closure
    arXiv.cs.SI Pub Date : 2020-11-16
    Alberto Aleta; Marta Tuninetti; Daniela Paolotti; Yamir Moreno; Michele Starnini

    Link prediction algorithms can help to understand the structure and dynamics of complex systems, to reconstruct networks from incomplete data sets and to forecast future interactions in evolving networks. Available algorithms based on similarity between nodes are bounded by the limited amount of links present in these networks. In this work, we reduce this latter intrinsic limitation and show that

    更新日期:2020-11-19
  • On Influencing the Influential: Disparity Seeding
    arXiv.cs.SI Pub Date : 2020-11-17
    Arwen Teng; Ting-Wei Li; Yu-chi Liao; Hsi-Wen Chen; Yvonne-Anne Pignolet; De-Nian Yang; Lydia Y. Chen

    Online social network platforms have become a crucial medium to disseminate the latest political, commercial, and social information. Users with high visibility are often selected as seeds to spread information and affect their adoption in target groups. The central theme of this paper is to answer how gender differences and similarities can impact the information spreading process. To this end, we

    更新日期:2020-11-19
  • Allocating marketing resources over social networks: A long-term analysis
    arXiv.cs.SI Pub Date : 2020-11-17
    Vineeth S. Varma; Samson Lasaulce; Julien Mounthanyvong; Irinel-Constantin Morarescu

    In this paper, we consider a network of consumers who are under the combined influence of their neighbors and external influencing entities (the marketers). The consumers' opinion follows a hybrid dynamics whose opinion jumps are due to the marketing campaigns. By using the relevant static game model proposed recently in [1], we prove that although the marketers are in competition and therefore create

    更新日期:2020-11-19
  • Addressing Computational Bottlenecks in Higher-Order Graph Matching with Tensor Kronecker Product Structure
    arXiv.cs.SI Pub Date : 2020-11-17
    Charles Colley; Huda Nassar; David Gleich

    Graph matching, also known as network alignment, is the problem of finding a correspondence between the vertices of two separate graphs with strong applications in image correspondence and functional inference in protein networks. One class of successful techniques is based on tensor Kronecker products and tensor eigenvectors. A challenge with these techniques are memory and computational demands that

    更新日期:2020-11-18
  • Political Partisanship and Anti-Science Attitudes in Online Discussions about Covid-19
    arXiv.cs.SI Pub Date : 2020-11-17
    Ashwin Rao; Fred Morstatter; Minda Hu; Emily Chen; Keith Burghardt; Emilio Ferrara; Kristina Lerman

    The novel coronavirus pandemic continues to ravage communities across the US. Opinion surveys identified importance of political ideology in shaping perceptions of the pandemic and compliance with preventive measures. Here, we use social media data to study complexity of polarization. We analyze a large dataset of tweets related to the pandemic collected between January and May of 2020, and develop

    更新日期:2020-11-18
  • Conspiracy and debunking narratives about COVID-19 origination on Chinese social media: How it started and who is to blame
    arXiv.cs.SI Pub Date : 2020-11-17
    Kaiping Chen; Anfan Chen; Jingwen Zhang; Jingbo Meng; Cuihua Shen

    This paper studies conspiracy and debunking narratives about COVID-19 origination on a major Chinese social media platform, Weibo, from January to April 2020. Popular conspiracies about COVID-19 on Weibo, including that the virus is human-synthesized or a bioweapon, differ substantially from those in the US. They attribute more responsibility to the US than to China, especially following Sino-US confrontations

    更新日期:2020-11-18
  • Design Space for Graph Neural Networks
    arXiv.cs.SI Pub Date : 2020-11-17
    Jiaxuan You; Rex Ying; Jure Leskovec

    The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications. However, current research focuses on proposing and evaluating specific architectural designs of GNNs, as opposed to studying the more general design space of GNNs that consists of a Cartesian product of different design dimensions, such as the number of layers or the type

    更新日期:2020-11-18
  • New (k,l,m)-verifiable multi-secret sharing schemes based on XTR public key system
    arXiv.cs.SI Pub Date : 2020-11-17
    Jing Yang; Fang-Wei Fu

    Secret sharing was proposed primarily in 1979 to solve the problem of key distribution. In recent decades, researchers have proposed many improvement schemes. Among all these schemes, the verifiable multi-secret sharing (VMSS) schemes are studied sufficiently, which share multiple secrets simultaneously and perceive malicious dealer as well as participants. By pointing out that the schemes presented

    更新日期:2020-11-18
  • Space-time budget allocation policy design for viral marketing
    arXiv.cs.SI Pub Date : 2020-11-17
    I. C. Morarescu; V. S. Varma; L. Busoniu; S. Lasaulce

    We address formally the problem of opinion dynamics when the agents of a social network (e.g., consumers) are not only influenced by their neighbors but also by an external influential entity referred to as a marketer. The influential entity tries to sway the overall opinion as close as possible to a desired opinion by using a specific influence budget. We assume that the exogenous influences of the

    更新日期:2020-11-18
  • Marketing resource allocation in duopolies over social networks
    arXiv.cs.SI Pub Date : 2020-11-17
    Vineeth S. Varma; Irinel-Constantin Morarescu; Samson Lasaulce; Samuel Martin

    One of the key features of this paper is that the agents' opinion of a social network is assumed to be not only influenced by the other agents but also by two marketers in competition. One of our contributions is to propose a pragmatic game-theoretical formulation of the problem and to conduct the complete corresponding equilibrium analysis (existence, uniqueness, dynamic characterization, and determination)

    更新日期:2020-11-18
  • A Model of Polarization on Social Media Caused by Empathy and Repulsion
    arXiv.cs.SI Pub Date : 2020-11-16
    Naoki Hirakura; Masaki Aida; Konosuke Kawashima

    In recent years, the ease with which social media can be accessed has led to the unexpected problem of a shrinkage in information sources. This phenomenon is caused by a system that facilitates the connection of people with similar ideas and recommendation systems. Bias in the selection of information sources promotes polarization that divides people into multiple groups with opposing views and creates

    更新日期:2020-11-17
  • Technology to Counter Online Flaming Based on the Frequency-Dependent Damping Coefficient in the Oscillation Model
    arXiv.cs.SI Pub Date : 2020-11-16
    Shinichi Kikuchi; Chisa Takano; Masaki Aida

    Online social networks, which are remarkably active, often experience explosive user dynamics such as online flaming, which can significantly impact the real world. However, countermeasures based on social analyses of the individuals causing flaming are too slow to be effective because of the rapidity with which the influence of online user dynamics propagates. A countermeasure technology for the flaming

    更新日期:2020-11-17
  • Strongly Connected Components in Stream Graphs: Computation and Experimentations
    arXiv.cs.SI Pub Date : 2020-11-16
    Léo Rannou; Clémence Magnien; Matthieu Latapy

    Stream graphs model highly dynamic networks in which nodes and/or links arrive and/or leave over time. Strongly connected components in stream graphs were defined recently, but no algorithm was provided to compute them. We present here several solutions with polynomial time and space complexities, each with its own strengths and weaknesses. We provide an implementation and experimentally compare the

    更新日期:2020-11-17
  • SSNE: Effective Node Representation for Link Prediction in Sparse Networks
    arXiv.cs.SI Pub Date : 2020-11-16
    Min-Ren Chen; Ping Huang; Yu Lin; Shi-Min Cai

    Graph embedding is gaining its popularity for link prediction in complex networks and achieving excellent performance. However, limited work has been done in sparse networks that represent most of real networks. In this paper, we propose a model, Sparse Structural Network Embedding (SSNE), to obtain node representation for link predication in sparse networks. The SSNE first transforms the adjacency

    更新日期:2020-11-17
  • Strongly Local Hypergraph Diffusions for Clustering and Semi-supervised Learning
    arXiv.cs.SI Pub Date : 2020-11-16
    Meng Liu; Nate Veldt; Haoyu Song; Pan Li; David F. Gleich

    Hypergraph-based machine learning methods are now widely recognized as important for modeling and using higher-order and multiway relationships between data objects. Local hypergraph clustering and semi-supervised learning specifically involve finding a well-connected set of nodes near a given set of labeled vertices. Although many methods for local clustering exist for graphs, there are relatively

    更新日期:2020-11-17
  • Social Contagion and Associative Diffusion in Multilayer Network
    arXiv.cs.SI Pub Date : 2020-11-16
    Heng-Chien Liou; Hsuan-Wei Lee

    The question that how cultural variation emerges has drawn lots of interest in sociological inquiry. Sociologists predominantly study such variation through the lens of social contagion, which mostly attributes cultural variation to the underlying structural segregation, making it epiphenomenal to the pre-existing segregated structure. On the other hand, arguing culture doesn't spread like a virus

    更新日期:2020-11-17
  • Spatial Social Network (SSN) Hot Spot Detection: Scan Methods for Non-Planar Networks
    arXiv.cs.SI Pub Date : 2020-11-16
    Joshua Baker; Clio Andris; Daniel DellaPosta

    Moving window and hot spot detection analyses are statistical methods used to analyze point patterns within a given area. Such methods have been used to successfully detect clusters of point events such as car thefts or incidences of cancer. Yet, these methods do not account for the connections between individual events, such as social ties within a neighborhood. This paper presents two GIS methods

    更新日期:2020-11-17
  • Influence of User Emotion on Information Propagation with Public Sentiment in the Chinese Sina-microblog
    arXiv.cs.SI Pub Date : 2020-11-16
    Fulian Yin

    Social networks are flooded with different pieces of emotional information, the propagation of which helps to shape the development of public sentiment. To help designing effective communication strategies during the entire development of an event,we propose an emotion-based susceptible-forwarding-immune (E-SFI) propagation dynamic model, that takes into account of the categories of emotions into positive

    更新日期:2020-11-17
  • Centrality Measures in Complex Networks: A Survey
    arXiv.cs.SI Pub Date : 2020-11-14
    Akrati Saxena; Sudarshan Iyengar

    In complex networks, each node has some unique characteristics that define the importance of the node based on the given application-specific context. These characteristics can be identified using various centrality metrics defined in the literature. Some of these centrality measures can be computed using local information of the node, such as degree centrality and semi-local centrality measure. Others

    更新日期:2020-11-17
  • Expertise and confidence explain how social influence evolves along intellective tasks
    arXiv.cs.SI Pub Date : 2020-11-13
    Omid Askarisichani; Elizabeth Y. Huang; Kekoa S. Sato; Noah E. Friedkin; Francesco Bullo; Ambuj K. Singh

    Discovering the antecedents of individuals' influence in collaborative environments is an important, practical, and challenging problem. In this paper, we study interpersonal influence in small groups of individuals who collectively execute a sequence of intellective tasks. We observe that along an issue sequence with feedback, individuals with higher expertise and social confidence are accorded higher

    更新日期:2020-11-17
  • Causal motifs and existence of endogenous cascades in directed networks with application to company defaults
    arXiv.cs.SI Pub Date : 2020-11-16
    Irena Barjašić; Hrvoje Štefančić; Vedrana Pribičević; Vinko Zlatić

    Motivated by detection of cascades of defaults in economy, we developed a detection framework for endogenous spreading based on causal motifs we define in this paper. We assume that vertex change of state can be triggered by endogenous or exogenous event, that underlying network is directed and that times when vertices changed their states are available. In addition to data of company defaults we use

    更新日期:2020-11-17
  • PanCast: Listening to Bluetooth Beacons for Epidemic Risk Mitigation
    arXiv.cs.SI Pub Date : 2020-11-16
    Gilles Barthe; Roberta De Viti; Peter Druschel; Deepak Garg; Manuel Gomez-Rodriguez; Pierfrancesco Ingo; Matthew Lentz; Aastha Mehta; Bernhard Schölkopf

    During the ongoing COVID-19 pandemic, there have been burgeoning efforts to develop and deploy smartphone apps to expedite contact tracing and risk notification. Most of these apps track pairwise encounters between individuals via Bluetooth and then use these tracked encounters to identify and notify those who might have been in proximity of a contagious individual. Unfortunately, these apps have not

    更新日期:2020-11-17
  • A Large-Scale Database for Graph Representation Learning
    arXiv.cs.SI Pub Date : 2020-11-16
    Scott Freitas; Yuxiao Dong; Joshua Neil; Duen Horng Chau

    With the rapid emergence of graph representation learning, the construction of new large-scale datasets are necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique. By carefully analyzing existing graph databases, we identify 3 critical components important for advancing the field of graph representation learning: (1) large graphs, (2) many graphs

    更新日期:2020-11-17
  • Quantifying Community Resilience Based on Fluctuations in Visits to Point-of-Interest from Digital Trace Data
    arXiv.cs.SI Pub Date : 2020-11-15
    Cristian PodestaUrban Resilience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University; Natalie ColemanUrban Resilience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University; Amir EsmalianUrban Resilience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University; Fax YuanUrban Resilience.AI Lab, Zachry Department

    This study aims to quantify community resilience based on fluctuations in the visits to various Point-of-Interest (POIs) locations. Visit to POIs is an essential indicator of human activities and captures the combined effects of perturbations in people lifestyles, built environment conditions, and businesses status. The study utilized digital trace data of unique visits to POIs in the context of the

    更新日期:2020-11-17
  • International expert communities on Twitter become more isolated during the COVID-19 pandemic
    arXiv.cs.SI Pub Date : 2020-11-13
    Francesco Durazzi; Martin Müller; Marcel Salathé; Daniel Remondini

    COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. In characterizing the complex retweet network, we identify several stable communities, and are able to link them to scientific expert

    更新日期:2020-11-16
  • Quantify Influence of Delay in Opinion Transmission of Opinion Leaders on COVID-19 Information Propagation in the Chinese Sina-microblog
    arXiv.cs.SI Pub Date : 2020-11-13
    Fulian Yin; Xueying Shao; Meiqi Ji; Jianhong Wu

    In a fast evolving major public health crisis such as the COVID-19 pandemic, multiple pieces of relevant information can be posted sequentially in a social media platform. The interval between subsequent posting times may have different impact on the transmission and cross-propagation of the old and new information to result in different peak value and final size of forwarding users of the new information

    更新日期:2020-11-16
  • A Mixed-Method Landscape Analysis of SME-focused B2B Platforms in Germany
    arXiv.cs.SI Pub Date : 2020-11-13
    Tina Krell; Fabian Braesemann; Fabian Stephany; Nicolas Friederici; Philip Meier

    Digital platforms offer vast potential for increased value creation and innovation, especially through cross-organizational data sharing. It appears that SMEs in Germany are currently hesitant or unable to create their own platforms. To get a holistic overview of the structure of the German SME-focused platform landscape (that is platforms that are led by or targeting SMEs), we applied a mixed method

    更新日期:2020-11-16
  • Similarity network fusion for scholarly journals
    arXiv.cs.SI Pub Date : 2020-11-13
    Federica Baccini; Lucio Barabesi; Alberto Baccini; Mahdi Khelfaoui; Yves Gingras

    This paper explores intellectual and social proximity among scholarly journals by using network fusion techniques. Similarities among journals are initially represented by means of a three-layer network based on co-citations, common authors and common editors. The information contained in the three layers is combined by implementing a fused similarity network. Subsequently, partial distance correlations

    更新日期:2020-11-16
  • Influencing dynamics on social networks without knowledge of network microstructure
    arXiv.cs.SI Pub Date : 2020-11-11
    Matthew Garrod; Nick S. Jones

    Social network based information campaigns can be used for promoting beneficial health behaviours and mitigating polarisation (e.g. regarding climate change or vaccines). Network-based intervention strategies typically rely on full knowledge of network structure. It is largely not possible or desirable to obtain population-level social network data due to availability and privacy issues. It is easier

    更新日期:2020-11-16
  • Identifying influential nodes in complex networks: Effective distance gravity model
    arXiv.cs.SI Pub Date : 2020-11-12
    Qiuyan Shang; Yong Deng; Kang Hao Cheong

    The identification of important nodes in complex networks is an area of exciting growth due to its applications across various disciplines like disease controlling, community finding, data mining, network system controlling, just to name a few. Many measures have thus been proposed to date, and these measures are either based on the locality of nodes or the global nature of the network. These measures

    更新日期:2020-11-13
  • Sentiment Diffusion in Financial News Networks and Associated Market Movements
    arXiv.cs.SI Pub Date : 2020-11-05
    Xingchen Wan; Jie Yang; Slavi Marinov; Jan-Peter Calliess; Stefan Zohren; Xiaowen Dong

    In an increasingly connected global market, news sentiment towards one company may not only indicate its own market performance, but can also be associated with a broader movement on the sentiment and performance of other companies from the same or even different sectors. In this paper, we apply NLP techniques to understand news sentiment of 87 companies among the most reported on Reuters for a period

    更新日期:2020-11-13
  • Multi-View Dynamic Heterogeneous Information Network Embedding
    arXiv.cs.SI Pub Date : 2020-11-12
    Zhenghao Zhang; Jianbin Huang; Qinglin Tan

    Most existing Heterogeneous Information Network (HIN) embedding methods focus on static environments while neglecting the evolving characteristic of realworld networks. Although several dynamic embedding methods have been proposed, they are merely designed for homogeneous networks and cannot be directly applied in heterogeneous environment. To tackle above challenges, we propose a novel framework for

    更新日期:2020-11-13
  • Football tracking networks: Beyond event-based connectivity
    arXiv.cs.SI Pub Date : 2020-11-11
    J. M. Buldu; D. Garrido; D. R. Antequera; J. Busquets; E. Estrada; R. Resta; R. Lopez del Campo

    We propose using Network Science as a complementary tool to analyze player and team behavior during a football match. Specifically, we introduce four kinds of networks based on different ways of interaction between players. Our approach's main novelty is to use tracking datasets to create football tracking networks, instead of constructing and analyzing the traditional networks based on events. In

    更新日期:2020-11-13
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