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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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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’

  • 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

  • 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

  • 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:

  • 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

  • Optimization problems in correlated networks.
    Comput. Soc. Netw. Pub Date : 2016-01-01
    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

  • A game theory-based trust measurement model for social networks.
    Comput. Soc. Netw. Pub Date : 2016-01-01
    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

  • Network characteristics emerging from agent interactions in balanced distributed system.
    Comput. Soc. Netw. Pub Date : 2015-01-01
    Mahdi Abed Salman,Cyrille Bertelle,Eric Sanlaville

    A distributed computing system behaves like a complex network, the interactions between nodes being essential information exchanges and migrations of jobs or services to execute. These actions are performed by software agents, which behave like the members of social networks, cooperating and competing to obtain knowledge and services. The load balancing consists in distributing the load evenly between

  • Analysis and control of information diffusion dictated by user interest in generalized networks.
    Comput. Soc. Netw. Pub Date : 2015-01-01
    Eleni Stai,Vasileios Karyotis,Symeon Papavassiliou

    The diffusion of useful information in generalized networks, such as those consisting of wireless physical substrates and social network overlays is very important for theoretical and practical applications. Contrary to previous works, we focus on the impact of user interest and its features (e.g., interest periodicity) on the dynamics and control of diffusion of useful information within such complex

  • Cascade source inference in networks: a Markov chain Monte Carlo approach.
    Comput. Soc. Netw. Pub Date : 2015-01-01
    Xuming Zhai,Weili Wu,Wen Xu

    Cascades of information, ideas, rumors, and viruses spread through networks. Sometimes, it is desirable to find the source of a cascade given a snapshot of it. In this paper, source inference problem is tackled under Independent Cascade (IC) model. First, the #P-completeness of source inference problem is proven. Then, a Markov chain Monte Carlo algorithm is proposed to find a solution. It is worth

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • Online network organization of Barcelona en Comú, an emergent movement-party.
    Comput. Soc. Netw. Pub Date : 2017-09-18
    Pablo Aragón,Helena Gallego,David Laniado,Yana Volkovich,Andreas Kaltenbrunner

    The emerging grassroots party Barcelona en Comú won the 2015 Barcelona City Council election. This candidacy was devised by activists involved in the Spanish 15M movement to transform citizen outrage into political change. On the one hand, the 15M movement was based on a decentralized structure. On the other hand, political science literature postulates that parties develop oligarchical leadership

  • Hierarchical community detection via rank-2 symmetric nonnegative matrix factorization.
    Comput. Soc. Netw. Pub Date : 2017-09-08
    Rundong Du,Da Kuang,Barry Drake,Haesun Park

    Community discovery is an important task for revealing structures in large networks. The massive size of contemporary social networks poses a tremendous challenge to the scalability of traditional graph clustering algorithms and the evaluation of discovered communities. We propose a divide-and-conquer strategy to discover hierarchical community structure, nonoverlapping within each level. Our algorithm

  • Stance and influence of Twitter users regarding the Brexit referendum.
    Comput. Soc. Netw. Pub Date : 2017-07-24
    Miha Grčar,Darko Cherepnalkoski,Igor Mozetič,Petra Kralj Novak

    Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit referendum, held in the UK in June 2016. We address two questions: what is the relation between the Twitter mood and the referendum outcome, and who were the most influential Twitter users in

  • Computation and analysis of temporal betweenness in a knowledge mobilization network.
    Comput. Soc. Netw. Pub Date : 2017-07-10
    Amir Afrasiabi Rad,Paola Flocchini,Joanne Gaudet

    Highly dynamic social networks, where connectivity continuously changes in time, are becoming more and more pervasive. Knowledge mobilization, which refers to the use of knowledge toward the achievement of goals, is one of the many examples of dynamic social networks. Despite the wide use and extensive study of dynamic networks, their temporal component is often neglected in social network analysis

  • Using attractiveness model for actors ranking in social media networks.
    Comput. Soc. Netw. Pub Date : 2017-06-26
    Ziyaad Qasem,Marc Jansen,Tobias Hecking,H Ulrich Hoppe

    Influential actors detection in social media such as Twitter or Facebook can play a major role in gathering opinions on particular topics, improving the marketing efficiency, predicting the trends, etc. This work aims to extend our formally defined T measure to present a new measure aiming to recognize the actor’s influence by the strength of attracting new important actors into a networked community

  • Modelling and analysis of the dynamics of adaptive temporal-causal network models for evolving social interactions.
    Comput. Soc. Netw. Pub Date : 2017-06-12
    Jan Treur

    Network-Oriented Modelling based on adaptive temporal–causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes. Adaptive temporal–causal network models are based on causal relations by which the states in the network change over time, and these causal relations are adaptive in the sense that they

  • Effect of direct reciprocity and network structure on continuing prosperity of social networking services.
    Comput. Soc. Netw. Pub Date : 2017-05-26
    Kengo Osaka,Fujio Toriumi,Toshihauru Sugawara

    Social networking services (SNSs) are widely used as communicative tools for a variety of purposes. SNSs rely on the users’ individual activities associated with some cost and effort, and thus it is not known why users voluntarily continue to participate in SNSs. Because the structures of SNSs are similar to that of the public goods (PG) game, some studies have focused on why voluntary activities emerge

  • Measuring the value of accurate link prediction for network seeding.
    Comput. Soc. Netw. Pub Date : 2017-05-18
    Yijin Wei,Gwen Spencer

    The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction

  • Detection of strong attractors in social media networks.
    Comput. Soc. Netw. Pub Date : 2016-12-07
    Ziyaad Qasem,Marc Jansen,Tobias Hecking,H Ulrich Hoppe

    Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing. The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model

  • Text normalization for named entity recognition in Vietnamese tweets.
    Comput. Soc. Netw. Pub Date : 2016-12-01
    Vu H Nguyen,Hien T Nguyen,Vaclav Snasel

    Named entity recognition (NER) is a task of detecting named entities in documents and categorizing them to predefined classes, such as person, location, and organization. This paper focuses on tweets posted on Twitter. Since tweets are noisy, irregular, brief, and include acronyms and spelling errors, NER in those tweets is a challenging task. Many approaches have been proposed to deal with this problem

  • Why continuous discussion can promote the consensus of opinions?
    Comput. Soc. Netw. Pub Date : 2016-11-21
    Zhenpeng Li,Xijin Tang,Benhui Chen,Jian Yang,Peng Su

    Why group opinions tend to be converged through continued communication, discussion and interactions? Under the framework of the social influence network model, we rigorously prove that the group consensus is almost surely within finite steps. This is a quite certain result, and reflects the real-world common phenomenon. In addition, we give a convergence time lower bound. Although our explanations

  • Real-time topic-aware influence maximization using preprocessing.
    Comput. Soc. Netw. Pub Date : 2016-11-10
    Wei Chen,Tian Lin,Cheng Yang

    Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users are often topic-dependent and information, ideas, innovations etc. being propagated

  • An efficient method for link prediction in weighted multiplex networks.
    Comput. Soc. Netw. Pub Date : 2016-11-05
    Shikhar Sharma,Anurag Singh

    A great variety of artificial and natural systems can be abstracted into a set of entities interacting with each other. Such abstractions can very well represent the underlying dynamics of the system when modeled as the network of vertices coupled by edges. Prediction of dynamics in these structures based on topological attribute or dependency relations is an important task. Link Prediction in such

  • Clustering 1-dimensional periodic network using betweenness centrality.
    Comput. Soc. Netw. Pub Date : 2016-10-21
    Norie Fu,Vorapong Suppakitpaisarn

    While the temporal networks have a wide range of applications such as opportunistic communication, there are not many clustering algorithms specifically proposed for them. Based on betweenness centrality for periodic graphs, we give a clustering pseudo-polynomial time algorithm for temporal networks, in which the transit value is always positive and the least common multiple of all transit values is

  • Information fusion-based approach for studying influence on Twitter using belief theory.
    Comput. Soc. Netw. Pub Date : 2016-09-22
    Lobna Azaza,Sergey Kirgizov,Marinette Savonnet,Éric Leclercq,Nicolas Gastineau,Rim Faiz

    Influence in Twitter has become recently a hot research topic, since this micro-blogging service is widely used to share and disseminate information. Some users are more able than others to influence and persuade peers. Thus, studying most influential users leads to reach a large-scale information diffusion area, something very useful in marketing or political campaigns. In this study, we propose a

  • Factorization threshold models for scale-free networks generation.
    Comput. Soc. Netw. Pub Date : 2016-08-22
    Akmal Artikov,Aleksandr Dorodnykh,Yana Kashinskaya,Egor Samosvat

    Several models for producing scale-free networks have been suggested; most of them are based on the preferential attachment approach. In this article, we suggest a new approach for generating scale-free networks with an alternative source of the power-law degree distribution. The model derives from matrix factorization methods and geographical threshold models that were recently proven to show good

  • A hashtag recommendation system for twitter data streams.
    Comput. Soc. Netw. Pub Date : 2016-05-31
    Eriko Otsuka,Scott A Wallace,David Chiu

    Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now. A hashtag, a keyword prefixed with a hash symbol (#), is a feature in Twitter to organize tweets and facilitate effective search among a massive volume of data. In this paper, we propose an automatic hashtag recommendation system that helps users

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