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Modeling the transmission dynamics of racism propagation with community resilience Comput. Soc. Netw. Pub Date : 2021-11-06 Mamo, Dejen Ketema
Racism spreading can have a vital influence on people’s lives, declining adherence, pretending political views, and recruiters’ socio-economical crisis. Besides, Web 2.0 technologies have democratized the creation and propagation of racist information, which facilitated the rapid spreading of racist messages. In this research work, the impact of community resilience on the spread dynamics of racism
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Contextual polarity and influence mining in online social networks Comput. Soc. Netw. Pub Date : 2021-10-14 Alzahrani, Hassan, Acharya, Subrata, Duverger, Philippe, Nguyen, Nam P.
Crowdsourcing is an emerging tool for collaboration and innovation platforms. Recently, crowdsourcing platforms have become a vital tool for firms to generate new ideas, especially large firms such as Dell, Microsoft, and Starbucks, Crowdsourcing provides firms with multiple advantages, notably, rapid solutions, cost savings, and a variety of novel ideas that represent the diversity inherent within
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Fairness norm through social networks: a simulation approach Comput. Soc. Netw. Pub Date : 2021-10-09 Rifki, Omar, Ono, Hirotaka
Recently there has been an increased interest in adopting game-theoretic models to social norms. Most of these approaches are generally lacking a structure linking the local level of the ‘norm’ interaction to its global ‘social’ nature. Although numerous studies examined local-interaction games, where the emphasis is placed on neighborhood relations, regarding social network as a whole unique entity
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A model for the co-evolution of dynamic social networks and infectious disease dynamics Comput. Soc. Netw. Pub Date : 2021-10-07 Nunner, Hendrik, Buskens, Vincent, Kretzschmar, Mirjam
Recent research shows an increasing interest in the interplay of social networks and infectious diseases. Many studies either neglect explicit changes in health behavior or consider networks to be static, despite empirical evidence that people seek to distance themselves from diseases in social networks. We propose an adaptable steppingstone model that integrates theories of social network formation
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Understanding how retweets influence the behaviors of social networking service users via agent-based simulation Comput. Soc. Netw. Pub Date : 2021-09-13 Yan, Yizhou, Toriumi, Fujio, Sugawara, Toshiharu
The retweet is a characteristic mechanism of several social network services/social media, such as Facebook, Twitter, and Weibo. By retweeting tweet, users can share an article with their friends and followers. However, it is not clear how retweets affect the dominant behaviors of users. Therefore, this study investigates the impact of retweets on the behavior of social media users from the perspective
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A robust optimization model for influence maximization in social networks with heterogeneous nodes Comput. Soc. Netw. Pub Date : 2021-08-27 Agha Mohammad Ali Kermani, Mehrdad, Ghesmati, Reza, Pishvaee, Mir Saman
Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem, existing approaches deal with the concept of the expected number of nodes. In the current research, a scenario-based robust optimization approach is taken to finding the most influential
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Celebrity profiling through linguistic analysis of digital social networks Comput. Soc. Netw. Pub Date : 2021-08-26 Moreno-Sandoval, Luis G., Pomares-Quimbaya, Alexandra, Alvarado-Valencia, Jorge A.
Digital social networks have become an essential source of information because celebrities use them to share their opinions, ideas, thoughts, and feelings. This makes digital social networks one of the preferred means for celebrities to promote themselves and attract new followers. This paper proposes a model of feature selection for the classification of celebrities profiles based on their use of
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Link weights recovery in heterogeneous information networks Comput. Soc. Netw. Pub Date : 2021-03-23 Hong-Lan Botterman, Robin Lamarche-Perrin
Socio-technical systems usually consist of many intertwined networks, each connecting different types of objects or actors through a variety of means. As these networks are co-dependent, one can take advantage of this entangled structure to study interaction patterns in a particular network from the information provided by other related networks. A method is, hence, proposed and tested to recover the
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Spheres of legislation: polarization and most influential nodes in behavioral context Comput. Soc. Netw. Pub Date : 2021-03-21 Andrew C. Phillips, Mohammad T. Irfan, Luca Ostertag-Hill
Game-theoretic models of influence in networks often assume the network structure to be static. In this paper, we allow the network structure to vary according to the underlying behavioral context. This leads to several interesting questions on two fronts. First, how do we identify different contexts and learn the corresponding network structures using real-world data? We focus on the U.S. Senate and
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Modelling community structure and temporal spreading on complex networks Comput. Soc. Netw. Pub Date : 2021-03-18 Vesa Kuikka
We present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability
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Utilizing the simple graph convolutional neural network as a model for simulating influence spread in networks Comput. Soc. Netw. Pub Date : 2021-03-17 Alexander V. Mantzaris, Douglas Chiodini, Kyle Ricketson
The ability for people and organizations to connect in the digital age has allowed the growth of networks that cover an increasing proportion of human interactions. The research community investigating networks asks a range of questions such as which participants are most central, and which community label to apply to each member. This paper deals with the question on how to label nodes based on the
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Modeling and analyzing users’ behavioral strategies with co-evolutionary process Comput. Soc. Netw. Pub Date : 2021-03-10 Yutaro Miura, Fujio Toriumi, Toshiharu Sugawara
Social networking services (SNSs) are constantly used by a large number of people with various motivations and intentions depending on their social relationships and purposes, and thus, resulting in diverse strategies of posting/consuming content on SNSs. Therefore, it is important to understand the differences of the individual strategies depending on their network locations and surroundings. For
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Network analysis of internal migration in Croatia Comput. Soc. Netw. Pub Date : 2021-03-04 Dino Pitoski, Thomas J. Lampoltshammer, Peter Parycek
Migration, and urbanization as its consequence, is among the most intricate political and scientific topics, predicted to have huge effects on human lives in the near future. Thus being said, previous works have mainly focused on international migration, and the research on internal migration outside of the US is scarce, and in the case of Europe—the ubiquitous center of migration affairs—only in its
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Influence maximization in social media networks concerning dynamic user behaviors via reinforcement learning Comput. Soc. Netw. Pub Date : 2021-02-22 Mengnan Chen, Qipeng P. Zheng, Vladimir Boginski, Eduardo L. Pasiliao
This study examines the influence maximization (IM) problem via information cascades within random graphs, the topology of which dynamically changes due to the uncertainty of user behavior. This study leverages the discrete choice model (DCM) to calculate the probabilities of the existence of the directed arc between any two nodes. In this IM problem, the DCM provides a good description and prediction
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The structure of co-publications multilayer network Comput. Soc. Netw. Pub Date : 2021-02-09 Ghislain Romaric Meleu, Paulin Yonta Melatagia
Using the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we
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Influence network design via multi-level optimization considering boundedly rational user behaviours in social media networks Comput. Soc. Netw. Pub Date : 2021-02-08 Guanxiang Yun, Qipeng P. Zheng, Vladimir Boginski, Eduardo L. Pasiliao
Social media networks have been playing an increasingly more important role for both socialization and information diffusion. Political campaign can gain more supporters by attracting more mass attention and influencing them directly, while commercial campaigns can increase their companies’ profits by expanding social media connection with new users. To build the optimal network structure to influence
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Discovering the maximum k-clique on social networks using bat optimization algorithm Comput. Soc. Netw. Pub Date : 2021-02-02 Akram Khodadadi, Shahram Saeidi
The k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach
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Gumbel-softmax-based optimization: a simple general framework for optimization problems on graphs Comput. Soc. Netw. Pub Date : 2021-02-01 Yaoxin Li, Jing Liu, Guozheng Lin, Yueyuan Hou, Muyun Mou, Jiang Zhang
In computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure, such that the designed objective function is optimized under some constraints. However, these problems are notorious for their hardness to solve, because most of them are NP-hard or NP-complete. Although traditional general methods such
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Understanding social media beyond text: a reliable practice on Twitter Comput. Soc. Netw. Pub Date : 2021-01-30 Qixuan Hou, Meng Han, Feiyang Qu, Jing Selena He
Social media provides high-volume and real-time data, which has been broadly used in diverse applications in sales, marketing, disaster management, health surveillance, etc. However, distinguishing between noises and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. The rich information
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A hybrid metaheuristic for solving asymmetric distance-constrained vehicle routing problem Comput. Soc. Netw. Pub Date : 2021-01-22 Ha-Bang Ban, Phuong Khanh Nguyen
The Asymmetric Distance-Constrained Vehicle Routing Problem (ADVRP) is NP-hard as it is a natural extension of the NP-hard Vehicle Routing Problem. In ADVRP problem, each customer is visited exactly once by a vehicle; every tour starts and ends at a depot; and the traveled distance by each vehicle is not allowed to exceed a predetermined limit. We propose a hybrid metaheuristic algorithm combining
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Non-submodular model for group profit maximization problem in social networks Comput. Soc. Netw. Pub Date : 2021-01-07 Jianming Zhu, Smita Ghosh, Weili Wu, Chuangen Gao
In social networks, there exist many kinds of groups in which people may have the same interests, hobbies, or political orientation. Sometimes, group decisions are made by simply majority, which means that most of the users in this group reach an agreement, such as US Presidential Elections. A group is called activated if $$\beta$$ percent of users are influenced in the group. Enterprise will gain
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A review: preprocessing techniques and data augmentation for sentiment analysis Comput. Soc. Netw. Pub Date : 2021-01-06 Huu-Thanh Duong, Tram-Anh Nguyen-Thi
In literature, the machine learning-based studies of sentiment analysis are usually supervised learning which must have pre-labeled datasets to be large enough in certain domains. Obviously, this task is tedious, expensive and time-consuming to build, and hard to handle unseen data. This paper has approached semi-supervised learning for Vietnamese sentiment analysis which has limited datasets. We have
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Node-weighted centrality: a new way of centrality hybridization Comput. Soc. Netw. Pub Date : 2020-11-13 Anuj Singh, Rishi Ranjan Singh, S. R. S. Iyengar
Centrality measures have been proved to be a salient computational science tool for analyzing networks in the last two to three decades aiding many problems in the domain of computer science, economics, physics, and sociology. With increasing complexity and vividness in the network analysis problems, there is a need to modify the existing traditional centrality measures. Weighted centrality measures
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Structural hole centrality: evaluating social capital through strategic network formation Comput. Soc. Netw. Pub Date : 2020-09-17 Faisal Ghaffar, Neil Hurley
Strategic network formation is a branch of network science that takes an economic perspective to the creation of social networks, considering that actors in a network form links in order to maximise some utility that they attain through their connections to other actors in the network. In particular, Jackson’s Connections model, writes an actor’s utility as a sum over all other actors that can be reached
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Solving the k-dominating set problem on very large-scale networks Comput. Soc. Netw. Pub Date : 2020-07-20 Minh Hai Nguyen, Minh Hoàng Hà, Diep N. Nguyen, The Trung Tran
The well-known minimum dominating set problem (MDSP) aims to construct the minimum-size subset of vertices in a graph such that every other vertex has at least one neighbor in the subset. In this article, we study a general version of the problem that extends the neighborhood relationship: two vertices are called neighbors of each other if there exists a path through no more than k edges between them
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A new model for calculating the maximum trust in Online Social Networks and solving by Artificial Bee Colony algorithm Comput. Soc. Netw. Pub Date : 2020-02-13 Shahram Saeidi
The social networks are widely used by millions of people worldwide. The trust concept is one of the most important issues in Social Network Analysis (SNA) which highly affects the quantity and quality of the inter-connections, decisions, and interactions among the users in e-commerce or recommendation systems. Many normative algorithms are developed to calculate the trust which most of them are complicated
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A model of opinion and propagation structure polarization in social media Comput. Soc. Netw. Pub Date : 2020-01-09 Hafizh A. Prasetya, Tsuyoshi Murata
The issue of polarization in online social media has been gaining attention in recent years amid the changing political landscapes of many parts of the world. Several studies empirically observed the existence of echo chambers in online social media, stimulating a slew of works that tries to model the phenomenon via opinion modeling. Here, we propose a model of opinion dynamics centered around the
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Network-based indices of individual and collective advising impacts in mathematics Comput. Soc. Netw. Pub Date : 2020-01-06 Alexander Semenov, Alexander Veremyev, Alexander Nikolaev, Eduardo L. Pasiliao, Vladimir Boginski
Advising and mentoring Ph.D. students is an increasingly important aspect of the academic profession. We define and interpret a family of metrics (collectively referred to as “a-indices”) that can potentially be applied to “ranking academic advisors” using the academic genealogical records of scientists, with the emphasis on taking into account not only the number of students advised by an individual
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Full command of a network by a new node: some results and examples Comput. Soc. Netw. Pub Date : 2019-12-26 Clara Grácio, Sara Fernandes, Luís Mário Lopes
We consider that a network of chaotic identical dynamical systems is connected to a new node. Depending on some properties of the network and on the way that connection is made, the new node may control the network. We consider a full-command connection and analyze the possibility of the network being full-commandable by the new node. For full-commandable networks, we define the full-command-window
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Robust communication network formation: a decentralized approach Comput. Soc. Netw. Pub Date : 2019-11-27 Christopher Diaz, Alexander Nikolaev, Abhinav Perla, Alexander Veremyev, Eduardo Pasiliao
The formation of robust communication networks between independently acting agents is of practical interest in multiple domains, for example, in sensor placement and Unmanned Aerial Vehicle communication. These are the cases where it is only feasible to have the communicating actors modify the network locally, i.e., without relying on the knowledge of the entire network structure and the other actors’
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Network robustness improvement via long-range links Comput. Soc. Netw. Pub Date : 2019-11-19 Vincenza Carchiolo, Marco Grassia, Alessandro Longheu, Michele Malgeri, Giuseppe Mangioni
Many systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this
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Graph convolutional networks: a comprehensive review Comput. Soc. Netw. Pub Date : 2019-11-10 Si Zhang, Hanghang Tong, Jiejun Xu, Ross Maciejewski
Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data
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An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning Comput. Soc. Netw. Pub Date : 2019-11-06 Han Hu, NhatHai Phan, Soon A. Chun, James Geller, Huy Vo, Xinyue Ye, Ruoming Jin, Kele Ding, Deric Kenne, Dejing Dou
Drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drug-abuse risk behavior at a population scale, such as among the population of Twitter users, can help us to monitor the trend of drug-abuse incidents. Unfortunately, traditional methods do not effectively detect drug-abuse risk behavior, given tweets. This is because:
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Homophily in networked agent-based models: a method to generate homophilic attribute distributions to improve upon random distribution approaches Comput. Soc. Netw. Pub Date : 2019-11-01 Marie Lisa Kapeller, Georg Jäger, Manfred Füllsack
In the standard situation of networked populations, link neighbours represent one of the main influences leading to social diffusion of behaviour. When distinct attributes coexist, not only the network structure, but also the distribution of these traits shape the typical neighbourhood of each individual. While assortativity refers to the formation of links between similar individuals inducing the
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A game theory-based trust measurement model for social networks. Comput. Soc. Netw. Pub Date : 2016-05-20 Yingjie Wang,Zhipeng Cai,Guisheng Yin,Yang Gao,Xiangrong Tong,Qilong Han
BACKGROUND In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent. Modeling trust needs to consider interaction history, recommendation, user behaviors and so on. Therefore, modeling trust is an important focus
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Optimization problems in correlated networks. Comput. Soc. Netw. Pub Date : 2016-01-22 Song Yang,Stojan Trajanovski,Fernando A Kuipers
BACKGROUND Solving the shortest path and min-cut problems are key in achieving high-performance and robust communication networks. Those problems have often been studied in deterministic and uncorrelated networks both in their original formulations as well as in several constrained variants. However, in real-world networks, link weights (e.g., delay, bandwidth, failure probability) are often correlated
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Efficiently counting complex multilayer temporal motifs in large-scale networks Comput. Soc. Netw. Pub Date : 2019-09-14 Hanjo D. Boekhout, Walter A. Kosters, Frank W. Takes
This paper proposes novel algorithms for efficiently counting complex network motifs in dynamic networks that are changing over time. Network motifs are small characteristic configurations of a few nodes and edges, and have repeatedly been shown to provide insightful information for understanding the meso-level structure of a network. Here, we deal with counting more complex temporal motifs in large-scale
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Prerequisite-related MOOC recommendation on learning path locating Comput. Soc. Netw. Pub Date : 2019-08-08 Yanxia Pang, Na Wang, Ying Zhang, Yuanyuan Jin, Wendi Ji, Wenan Tan
Prerequisite inadequacy causes more MOOC drop-out. As an effective method interfering with learning process, existing MOOC recommendation is mainly about subsequent learning objects that have not been learned before. This paper proposes a locating-based MOOC recommendation method with consideration on prerequisite relationship. It locates the target learner’s learning paths and provides prerequisite
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A privacy-preserving framework for ranked retrieval model Comput. Soc. Netw. Pub Date : 2019-07-24 Tong Yan, Yunpeng Gao, Nan Zhang
In this paper, we address privacy issues related to ranked retrieval model in web databases, each of which takes private attributes as part of input in the ranking function. Many web databases keep private attributes invisible to public and believe that the adversary is unable to reveal the private attribute values from query results. However, prior research (Rahman et al. in Proc VLDB Endow 8:1106–17
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Markov processes in blockchain systems Comput. Soc. Netw. Pub Date : 2019-07-02 Quan-Lin Li, Jing-Yu Ma, Yan-Xia Chang, Fan-Qi Ma, Hai-Bo Yu
In this paper, we develop a more general framework of block-structured Markov processes in the queueing study of blockchain systems, which can provide analysis both for the stationary performance measures and for the sojourn time of any transaction or block. In addition, an original aim of this paper is to generalize the two-stage batch-service queueing model studied in Li et al. (Blockchain queue
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A network science-based k-means++ clustering method for power systems network equivalence Comput. Soc. Netw. Pub Date : 2019-04-22 Dhruv Sharma, Krishnaiya Thulasiraman, Di Wu, John N. Jiang
Network equivalence is a technique useful for many areas including power systems. In many power system analyses, generation shift factor (GSF)-based bus clustering methods have been widely used to reduce the complexity of the equivalencing problem. GSF captures power flow on a line when power is injected at a node using bus to bus electrical distance. A more appropriate measure is the one which captures
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Text mining and determinants of sentiments: Twitter social media usage by traditional media houses in Uganda Comput. Soc. Netw. Pub Date : 2019-04-10 Frank Namugera, Ronald Wesonga, Peter Jehopio
Unstructured data generated from sources such as the social media and traditional text documents are increasing and form a larger proportion of unanalysed data especially in the developing countries. In this study, we analysed data received from the major print and non-print media houses in Uganda through the Twitter platform to generate non-trivial knowledge by using text mining analytics. We also
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Comparing the speed and accuracy of approaches to betweenness centrality approximation Comput. Soc. Netw. Pub Date : 2019-02-09 John Matta, Gunes Ercal, Koushik Sinha
Many algorithms require doing a large number of betweenness centrality calculations quickly, and accommodating this need is an active open research area. There are many different ideas and approaches to speeding up these calculations, and it is difficult to know which approach will work best in practical situations. The current study attempts to judge performance of betweenness centrality approximation
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Complex network of United States migration Comput. Soc. Netw. Pub Date : 2019-01-24 Batyr Charyyev, Mehmet Hadi Gunes
Economists and social scientists have studied the human migration extensively. However, the complex network of human mobility in the United States (US) is not studied in depth. In this paper, we analyze migration network between counties and states in the US between 2000 and 2015 to analyze the overall structure of US migration and yearly changes using temporal analysis. We aggregated network on different
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Influence spreading model used to analyse social networks and detect sub-communities. Comput. Soc. Netw. Pub Date : 2018-11-29 Vesa Kuikka
A dynamic influence spreading model is presented for computing network centrality and betweenness measures. Network topology, and possible directed connections and unequal weights of nodes and links, are essential features of the model. The same influence spreading model is used for community detection in social networks and for analysis of network structures. Weaker connections give rise to more sub-communities
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Network partitioning algorithms as cooperative games. Comput. Soc. Netw. Pub Date : 2018-10-28 Konstantin E Avrachenkov,Aleksei Y Kondratev,Vladimir V Mazalov,Dmytro G Rubanov
The paper is devoted to game-theoretic methods for community detection in networks. The traditional methods for detecting community structure are based on selecting dense subgraphs inside the network. Here we propose to use the methods of cooperative game theory that highlight not only the link density but also the mechanisms of cluster formation. Specifically, we suggest two approaches from cooperative
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Social learning for resilient data fusion against data falsification attacks. Comput. Soc. Netw. Pub Date : 2018-10-25 Fernando Rosas,Kwang-Cheng Chen,Deniz Gündüz
Internet of Things (IoT) suffers from vulnerable sensor nodes, which are likely to endure data falsification attacks following physical or cyber capture. Moreover, centralized decision-making and data fusion turn decision points into single points of failure, which are likely to be exploited by smart attackers. To tackle this serious security threat, we propose a novel scheme for enabling distributed
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Tracking online topics over time: understanding dynamic hashtag communities. Comput. Soc. Netw. Pub Date : 2018-10-19 Philipp Lorenz-Spreen,Frederik Wolf,Jonas Braun,Gourab Ghoshal,Nataša Djurdjevac Conrad,Philipp Hövel
Hashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. For their analysis, we apply methods from network science and propose novel tools for tracing their dynamics in time-dependent data. The observations are characterized by bursty behaviors in the increases and decreases of hashtag usage. These features can be reproduced
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Extended methods for influence maximization in dynamic networks. Comput. Soc. Netw. Pub Date : 2018-10-01 Tsuyoshi Murata,Hokuto Koga
The process of rumor spreading among people can be represented as information diffusion in social network. The scale of rumor spread changes greatly depending on starting nodes. If we can select nodes that contribute to large-scale diffusion, the nodes are expected to be important for viral marketing. Given a network and the size of the starting nodes, the problem of selecting nodes for maximizing
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Adding ReputationRank to member promotion using skyline operator in social networks. Comput. Soc. Netw. Pub Date : 2018-09-04 Jiping Zheng,Siman Zhang
To identify potential stars in social networks, the idea of combining member promotion with skyline operator attracts people’s attention. Some algorithms have been proposed to deal with this problem so far, such as skyline boundary algorithms in unequal-weighted social networks. We propose an improved member promotion algorithm by presenting ReputationRank based on eigenvectors as well as Influence
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Assessing the role of participants in evolution of topic lifecycles on social networks. Comput. Soc. Netw. Pub Date : 2018-08-02 Kuntal Dey,Saroj Kaushik,Kritika Garg,Ritvik Shrivastava
Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts
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Including traffic jam avoidance in an agent-based network model. Comput. Soc. Netw. Pub Date : 2018-05-14 Christian Hofer,Georg Jäger,Manfred Füllsack
Understanding traffic is an important challenge in different scientific fields. While there are many approaches to constructing traffic models, most of them rely on origin–destination data and have difficulties when phenomena should be investigated that have an effect on the origin–destination matrix. A macroscopic traffic model is introduced that is novel in the sense that no origin–destination data
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Revisiting random walk based sampling in networks: evasion of burn-in period and frequent regenerations. Comput. Soc. Netw. Pub Date : 2018-03-19 Konstantin Avrachenkov,Vivek S Borkar,Arun Kadavankandy,Jithin K Sreedharan
In the framework of network sampling, random walk (RW) based estimation techniques provide many pragmatic solutions while uncovering the unknown network as little as possible. Despite several theoretical advances in this area, RW based sampling techniques usually make a strong assumption that the samples are in stationary regime, and hence are impelled to leave out the samples collected during the
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Towards distribution-based control of social networks. Comput. Soc. Netw. Pub Date : 2018-03-01 Dave McKenney,Tony White
Complex networks are found in many domains and the control of these networks is a research topic that continues to draw increasing attention. This paper proposes a method of network control that attempts to maintain a specified target distribution of the network state. In contrast to many existing network control research works, which focus exclusively on structural analysis of the network, this paper
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Consensus dynamics in online collaboration systems. Comput. Soc. Netw. Pub Date : 2018-02-01 Ilire Hasani-Mavriqi,Dominik Kowald,Denis Helic,Elisabeth Lex
In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profiles. Specifically, we are interested in how users similarity and their social status in the community, as well as the interplay of those two factors, influence the process of consensus dynamics
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Modelling the effect of religion on human empathy based on an adaptive temporal-causal network model. Comput. Soc. Netw. Pub Date : 2018-01-05 Laila van Ments,Peter Roelofsma,Jan Treur
Religion is a central aspect of many individuals’ lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal–causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time
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Steering opinion dynamics via containment control. Comput. Soc. Netw. Pub Date : 2017-11-27 Pietro DeLellis,Anna DiMeglio,Franco Garofalo,Francesco Lo Iudice
In this paper, we model the problem of influencing the opinions of groups of individuals as a containment control problem, as in many practical scenarios, the control goal is not full consensus among all the individual opinions, but rather their containment in a certain range, determined by a set of leaders. As in classical bounded confidence models, we consider individuals affected by the confirmation
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Coevolution of a multilayer node-aligned network whose layers represent different social relations. Comput. Soc. Netw. Pub Date : 2017-11-06 Ashwin Bahulkar,Boleslaw K Szymanski,Kevin Chan,Omar Lizardo
We examine the coevolution of three-layer node-aligned network of university students. The first layer is defined by nominations based on perceived prominence collected from repeated surveys during the first four semesters; the second is a behavioral layer representing actual students’ interactions based on records of mobile calls and text messages; while the third is a behavioral layer representing
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Controllability of social networks and the strategic use of random information. Comput. Soc. Netw. Pub Date : 2017-10-13 Marco Cremonini,Francesca Casamassima
This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected agents to random information is a technique already experimented in recommender systems or search engines, and represents one of the few options for influencing the behavior of a social context
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A method for evaluating discoverability and navigability of recommendation algorithms. Comput. Soc. Netw. Pub Date : 2017-10-11 Daniel Lamprecht,Markus Strohmaier,Denis Helic
Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no clear categorization of items exists. Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any recommendation evaluation measures