Computational Social Networks最新文献

{"title":"A network science-based k-means++ clustering method for power systems network equivalence","authors":"Dhruv Sharma, Krishnaiya Thulasiraman, Di Wu, John N. Jiang","doi":"10.1186/s40649-019-0064-3","DOIUrl":"https://doi.org/10.1186/s40649-019-0064-3","url":null,"abstract":"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 what may be called the electrical line distance with respect to a bus termed as relative bus to line distance. With increase in power transactions across different regions, the use of relative bus to line distance becomes appropriate for many analyses. Inspired by the recent trends in network science on the study of network dynamics based on the topological characteristics of a network, in this paper, we present a bus clustering method based on average electrical distance (AED). AED is independent of changes in location of slack bus and is based on the concept of electrical distance introduced in the context of molecular chemistry and pursued later for applications in social and complex networks. AED represents the AED from a bus to buses of the transmission line of interest. We first propose an AED-based method to group the buses into clusters for power systems network equivalence using k-means clustering algorithm integrated with silhouette analysis. One limitation of this method is that despite its speed, sometimes it may yield clusters of inferior quality compared to the optimal solution. To overcome this limitation, we next present our improved clustering method which incorporates a seeding technique that initializes centroids probabilistically. We also incorporate a technique in our method to find the number of clusters, k, to be given as input to our clustering algorithm. The resulting algorithm called AED-based k-means++ clustering method yields a clustering that is O(logk) competitive. Our network equivalence technique is next described. Finally, the efficacy of our new equivalencing technique is demonstrated by evaluating its performance on the IEEE 300-bus system and comparing that to the performance of our AED-based method (Sharma et al. in Power network equivalents: a network science-based k-means clustering method integrated with silhouette analysis. In: Complex networks and their application VI, Lyon, France. p. 78–89, 2017) and the existing GSF-based method.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"45 5","pages":"1-25"},"PeriodicalIF":0.0,"publicationDate":"2019-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text mining and determinants of sentiments: Twitter social media usage by traditional media houses in Uganda","authors":"Frank Namugera, R. Wesonga, Peter Jehopio","doi":"10.1186/s40649-019-0063-4","DOIUrl":"https://doi.org/10.1186/s40649-019-0063-4","url":null,"abstract":"","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-019-0063-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65734702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing the speed and accuracy of approaches to betweenness centrality approximation","authors":"John Matta, Gunes Ercal, K. Sinha","doi":"10.1186/s40649-019-0062-5","DOIUrl":"https://doi.org/10.1186/s40649-019-0062-5","url":null,"abstract":"","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-019-0062-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65734678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Complex network of United States migration","authors":"Batyr Charyyev, Mehmet Hadi Gunes","doi":"10.1186/s40649-019-0061-6","DOIUrl":"https://doi.org/10.1186/s40649-019-0061-6","url":null,"abstract":"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 time windows and analyzed for both county and state level. Analyzing flow between US counties and states, we focus on the migration during different periods such as economic prosperity of the housing boom and economic hardship of the housing bust. We observed that nodes at county and state level usually remain active, but there are considerable fluctuations on links. This indicates that migration patterns change over the time. However, we could identify a backbone at both county and state levels using disparity filter. Finally, we analyze impact of the political and socioeconomic factors on the migration. Using gravity model, we observe that population, political affiliation, poverty, and unemployment rate have influence on US migration.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"42 12","pages":"1-28"},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph convolutional networks: a comprehensive review.","authors":"Si Zhang,&nbsp;Hanghang Tong,&nbsp;Jiejun Xu,&nbsp;Ross Maciejewski","doi":"10.1186/s40649-019-0069-y","DOIUrl":"https://doi.org/10.1186/s40649-019-0069-y","url":null,"abstract":"<p><p>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 are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are often complex and diverse. On the other hand, the representation learning has achieved great successes in many areas. Thereby, a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space, such that the graph properties can be preserved. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. In this survey, despite numerous types of graph neural networks, we conduct a comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models. First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. Then, we categorize different graph convolutional networks according to the areas of their applications. Finally, we present several open challenges in this area and discuss potential directions for future research.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"6 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-019-0069-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71429016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking online topics over time: understanding dynamic hashtag communities.","authors":"Philipp Lorenz-Spreen,&nbsp;Frederik Wolf,&nbsp;Jonas Braun,&nbsp;Gourab Ghoshal,&nbsp;Nataša Djurdjevac Conrad,&nbsp;Philipp Hövel","doi":"10.1186/s40649-018-0058-6","DOIUrl":"https://doi.org/10.1186/s40649-018-0058-6","url":null,"abstract":"<p><strong>Background: </strong>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 with a novel model of dynamic rankings.</p><p><strong>Hashtag communities in time: </strong>We build temporal and weighted co-occurrence networks from hashtags. On static snapshots, we infer the community structure using customized methods. On temporal networks, we solve the bipartite matching problem of detected communities at subsequent timesteps by taking into account higher-order memory. This results in a matching protocol that is robust toward temporal fluctuations and instabilities of the static community detection. The proposed methodology is broadly applicable and its outcomes reveal the temporal behavior of online topics.</p><p><strong>Modeling topic-dynamics: </strong>We consider the size of the communities in time as a proxy for online popularity dynamics. We find that the distributions of gains and losses, as well as the interevent times are fat-tailed indicating occasional, but large and sudden changes in the usage of hashtags. Inspired by typical website designs, we propose a stochastic model that incorporates a ranking with respect to a time-dependent prestige score. This causes occasional cascades of rank shift events and reproduces the observations with good agreement. This offers an explanation for the observed dynamics, based on characteristic elements of online media.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-018-0058-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36714450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extended methods for influence maximization in dynamic networks.","authors":"Tsuyoshi Murata,&nbsp;Hokuto Koga","doi":"10.1186/s40649-018-0056-8","DOIUrl":"https://doi.org/10.1186/s40649-018-0056-8","url":null,"abstract":"<p><strong>Background: </strong>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 information diffusion is called influence maximization problem.</p><p><strong>Methods: </strong>We propose three new approximation methods (Dynamic Degree Discount, Dynamic CI, and Dynamic RIS) for influence maximization problem in dynamic networks. These methods are the extensions of previous methods for static networks to dynamic networks.</p><p><strong>Results: </strong>When compared with the previous methods, MC Greedy and Osawa, our proposed methods were found better than the previous methods: Although the performance of MC greedy was better than the three methods, it was computationally expensive and intractable for large-scale networks. The computational time of our proposed methods was more than 10 times faster than MC greedy, so they can be computed in realistic time even for large-scale dynamic networks. When compared with Osawa, the performances of these three methods were almost the same as Osawa, but they were approximately 7.8 times faster than Osawa.</p><p><strong>Conclusions: </strong>Based on these facts, the proposed methods are suitable for influence maximization in dynamic networks. Finding the strategies of choosing a suitable method for a given dynamic network is practically important. It is a challenging open question and is left for our future work. The problem of adjusting the parameters for Dynamic CI and Dynamic RIS is also left for our future work.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-018-0056-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36614324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards distribution-based control of social networks.","authors":"Dave McKenney,&nbsp;Tony White","doi":"10.1186/s40649-018-0052-z","DOIUrl":"https://doi.org/10.1186/s40649-018-0052-z","url":null,"abstract":"<p><strong>Background: </strong>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 also accounts for user actions/behaviours within the network control problem.</p><p><strong>Methods: </strong>This paper proposes and makes use of a novel distribution-based control method. The control approach is applied within a simulation of the real-valued voter model, which could have applications in problems such as the avoidance of consensus or extremism. The network control problem under consideration is investigated using various theoretical network types, including scale free, random, and small world.</p><p><strong>Results: </strong>It is argued that a distribution-based control approach may be more appropriate for several types of social control problems, in which the exact state of the system is of less interest than the overall system behaviour. The preliminary results presented in this paper demonstrate that a standard reinforcement learning approach is capable of learning a control signal selection policy to prevent the network state distribution from straying far from a specified target distribution.</p><p><strong>Conclusions: </strong>In summary, the results presented in this paper demonstrate the feasibility of a distribution-based control solution within the simulated problem. Additionally, several interesting questions arise from these results and are discussed as potential future work.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-018-0052-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35939733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adding <i>ReputationRank</i> to member promotion using skyline operator in social networks.","authors":"Jiping Zheng,&nbsp;Siman Zhang","doi":"10.1186/s40649-018-0055-9","DOIUrl":"https://doi.org/10.1186/s40649-018-0055-9","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>We propose an improved member promotion algorithm by presenting <i>ReputationRank</i> based on eigenvectors as well as <i>Influence</i> and <i>Activeness</i> and introduce the concept of skyline distance. Furthermore, we perform skyline operator over non-skyline set and choose the infra-skyline as our candidate set. The added <i>ReputationRank</i> helps a lot to describe the importance of a member while the skyline distance assists us to obtain the necessary condition for not being dominated so that some meaningless plans can be pruned.</p><p><strong>Results: </strong>Experiments on the DBLP and WikiVote datasets verify the effectiveness and efficiency of our proposed algorithm.</p><p><strong>Conclusions: </strong>Treating the infra-skyline set as candidate set reduces the number of candidates. The pruning strategies based on dominance and promotion cost decrease the searching space.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-018-0055-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36496389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network partitioning algorithms as cooperative games.","authors":"Konstantin E Avrachenkov,&nbsp;Aleksei Y Kondratev,&nbsp;Vladimir V Mazalov,&nbsp;Dmytro G Rubanov","doi":"10.1186/s40649-018-0059-5","DOIUrl":"https://doi.org/10.1186/s40649-018-0059-5","url":null,"abstract":"<p><p>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 game theory: the first approach is based on the Myerson value, whereas the second approach is based on hedonic games. Both approaches allow to detect clusters with various resolutions. However, the tuning of the resolution parameter in the hedonic games approach is particularly intuitive. Furthermore, the modularity-based approach and its generalizations as well as ratio cut and normalized cut methods can be viewed as particular cases of the hedonic games. Finally, for approaches based on potential hedonic games we suggest a very efficient computational scheme using Gibbs sampling.</p>","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"5 1","pages":"11"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40649-018-0059-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36665716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}