Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017最新文献

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Which friends are more popular than you?: Contact strength and the friendship paradox in social networks 哪些朋友比你更受欢迎?社交网络中的联系强度和友谊悖论

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-03-18 DOI: 10.1145/3110025.3110027

James P. Bagrow, C. Danforth, Lewis Mitchell

{"title":"Which friends are more popular than you?: Contact strength and the friendship paradox in social networks","authors":"James P. Bagrow, C. Danforth, Lewis Mitchell","doi":"10.1145/3110025.3110027","DOIUrl":"https://doi.org/10.1145/3110025.3110027","url":null,"abstract":"The friendship paradox states that in a social network, egos tend to have lower degree than their alters, or, \"your friends have more friends than you do\". Most research has focused on the friendship paradox and its implications for information transmission, but treating the network as static and unweighted. Yet, people can dedicate only a finite fraction of their attention budget to each social interaction: a high-degree individual may have less time to dedicate to individual social links, forcing them to modulate the quantities of contact made to their different social ties. Here we study the friendship paradox in the context of differing contact volumes between egos and alters, finding a connection between contact volume and the strength of the friendship paradox. The most frequently contacted alters exhibit a less pronounced friendship paradox compared with the ego, whereas less-frequently contacted alters are more likely to be high degree and give rise to the paradox. We argue therefore for a more nuanced version of the friendship paradox: \"your closest friends have slightly more friends than you do\", and in certain networks even: \"your best friend has no more friends than you do\". We demonstrate that this relationship is robust, holding in both a social media and a mobile phone dataset. These results have implications for information transfer and influence in social networks, which we explore using a simple dynamical model.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114918819","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}

引用次数: 12

Discovery, Retrieval, and Analysis of the 'Star Wars' Botnet in Twitter Twitter中“星球大战”僵尸网络的发现、检索和分析

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-01-10 DOI: 10.1145/3110025.3110074

J. Echeverría, Shi Zhou

引用次数: 63

Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings 别走，斯基普!多尺度网络嵌入的在线学习

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2016-05-06 DOI: 10.1145/3110025.3110086

Bryan Perozzi, Vivek Kulkarni, Haochen Chen, S. Skiena

{"title":"Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings","authors":"Bryan Perozzi, Vivek Kulkarni, Haochen Chen, S. Skiena","doi":"10.1145/3110025.3110086","DOIUrl":"https://doi.org/10.1145/3110025.3110086","url":null,"abstract":"We present WALKLETS, a novel approach for learning multiscale representations of vertices in a network. In contrast to previous works, these representations explicitly encode multi-scale vertex relationships in a way that is analytically derivable. WALKLETS generates these multiscale relationships by sub-sampling short random walks on the vertices of a graph. By 'skipping' over steps in each random walk, our method generates a corpus of vertex pairs which are reachable via paths of a fixed length. This corpus can then be used to learn a series of latent representations, each of which captures successively higher order relationships from the adjacency matrix. We demonstrate the efficacy of WALKLETS's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, DBLP, Flickr, and YouTube. Our results show that WALKLETS outperforms new methods based on neural matrix factorization. Specifically, we outperform DeepWalk by up to 10% and LINE by 58% Micro-F1 on challenging multi-label classification tasks. Finally, WALKLETS is an online algorithm, and can easily scale to graphs with millions of vertices and edges.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"14 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120814207","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}

引用次数: 164

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