Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017最新文献_第2页

Longitudinal Modeling of Social Media with Hawkes Process Based on Users and Networks 基于用户和网络的社交媒体Hawkes过程纵向建模

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110107

P. K. Srijith, M. Lukasik, Kalina Bontcheva, Trevor Cohn

引用次数: 17

Efficient Privacy-preserving Adversarial Learning in Decentralized Online Social Networks 分散在线社交网络中有效的隐私保护对抗学习

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3119400

Álvaro García-Recuero

{"title":"Efficient Privacy-preserving Adversarial Learning in Decentralized Online Social Networks","authors":"Álvaro García-Recuero","doi":"10.1145/3110025.3119400","DOIUrl":"https://doi.org/10.1145/3110025.3119400","url":null,"abstract":"In the last decade we have witnessed a more than prolific growth of online social media content in sites designed for online social interactions. These systems have been traditionally designed as centralized silos, which unfortunately suffer from abusive behavior ranging from spam, cyberbullying to even censorship. This paper investigates the utility of supervised learning techniques for abuse detection in future decentralized settings, where less metadata remains available for use in learning algorithms. We present a method that uses a privacy-preserving protocol to exchange a fingerprint of the neighborhood of a pair of nodes, namely sender and receiver. Our method extracts social graph metadata to form a subset of key features, namely neighborhood knowledge, some of which we approximate to reduce communication and computational requirements of such a protocol. In our benchmarking we show that a data minimization approach can obtain features 13% faster while providing similar or, as with the SVM classifier, even better abuse detection rates with just approximated Private Set Intersection.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115641516","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: 1

Optimizing the Effectiveness of Incentivized Social Sharing 优化激励性社会分享的有效性

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110147

Joseph J. Pfeiffer, E. Zheleva

引用次数: 1

Using Community Structure to Categorize Computer Science Conferences: Initial Results 使用社区结构对计算机科学会议进行分类:初步结果

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110102

Suhendry Effendy, R. Yap

引用次数: 0

Weak Ties Based Recommendation for Interdisciplinary Research Collaboration 基于弱联系的跨学科研究合作推荐

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3120990

Won Kyung Lee, S. Sohn

引用次数: 0

A Dynamic Algorithm for Updating Katz Centrality in Graphs 图中Katz中心性的动态更新算法

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110034

Eisha Nathan, David A. Bader

引用次数: 15

Dynamical Model of Flaming Phenomena in On-Line Social Networks 在线社交网络中燃烧现象的动力学模型

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3120982

M. Aida, C. Takano, M. Murata

引用次数: 9

Finding topical experts in Twitter via query-dependent personalized PageRank 通过查询依赖的个性化PageRank在Twitter上找到主题专家

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110044

Preethi Lahoti, G. D. F. Morales, A. Gionis

{"title":"Finding topical experts in Twitter via query-dependent personalized PageRank","authors":"Preethi Lahoti, G. D. F. Morales, A. Gionis","doi":"10.1145/3110025.3110044","DOIUrl":"https://doi.org/10.1145/3110025.3110044","url":null,"abstract":"Finding topical experts on micro-blogging sites, such as Twitter, is an essential information-seeking task. In this paper, we introduce an expert-finding algorithm for Twitter, which can be generalized to find topical experts in any social network with endorsement features. Our approach combines traditional link analysis with text mining. It relies on crowd-sourced data from Twitter lists to build a labeled directed graph called the endorsement graph, which captures topical expertise as perceived by users. Given a text query, our algorithm uses a dynamic topic-sensitive weighting scheme, which sets the weights on the edges of the graph. Then, it uses an improved version of query-dependent PageRank to find important nodes in the graph, which correspond to topical experts. In addition, we address the scalability and performance issues posed by large social networks by pruning the input graph via a focused-crawling algorithm. Extensive evaluation on a number of different topics demonstrates that the proposed approach significantly improves on query-dependent PageRank, outperforms the current publicly-known state-of-the-art methods, and is competitive with Twitter's own search system, while using less than 0.05% of all Twitter accounts.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125498549","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: 10

Measuring the return on communication investments on social media: The case of the higher education sector 衡量社交媒体上传播投资的回报:以高等教育部门为例

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3123027

Luciana Oliveira, Á. Figueira

引用次数: 1

Revisiting Resolution and Inter-Layer Coupling Factors in Modularity for Multilayer Networks 再论多层网络模块化中的分辨率和层间耦合因素

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110051

Alessia Amelio, Andrea Tagarelli

{"title":"Revisiting Resolution and Inter-Layer Coupling Factors in Modularity for Multilayer Networks","authors":"Alessia Amelio, Andrea Tagarelli","doi":"10.1145/3110025.3110051","DOIUrl":"https://doi.org/10.1145/3110025.3110051","url":null,"abstract":"Modularity for multilayer networks, also called multislice modularity, is parametric to a resolution factor and an inter-layer coupling factor. The former is useful to express layer-specific relevance and the latter quantifies the strength of node linkage across the layers of a network. However, such parameters can be set arbitrarily, thus discarding any structure information at graph or community level. Other issues are related to the inability of properly modeling order relations over the layers, which is required for dynamic networks. In this paper we propose a new definition of modularity for multilayer networks that aims to overcome major issues of existing multislice modularity. We revise the role and semantics of the layer-specific resolution and inter-layer coupling terms, and define parameter-free unsupervised approaches for their computation, by using information from the within-layer and inter-layer structures of the communities. Moreover, our formulation of multilayer modularity is general enough to account for an available ordering of the layers and relating constraints on layer coupling. Experimental evaluation was conducted using three state-of-the-art methods for multilayer community detection and nine real-world multilayer networks. Results have shown the significance of our modularity, disclosing the effects of different combinations of the resolution and inter-layer coupling functions. This work can pave the way for the development of new optimization methods for discovering community structures in multilayer networks.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130609326","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: 7