2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第7页

A multi-channel cybersecurity news and threat intelligent engine - SecBuzzer 多通道网络安全新闻和威胁智能引擎——SecBuzzer

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3345309

Shin-Ying Huang, Yennun Huang, Ching-Hao Mao

引用次数: 2

Data-driven Country Safety Monitoring Terrorist Attack Prediction 数据驱动的国家安全监测恐怖袭击预测

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343527

D. Spiliotopoulos, C. Vassilakis, Dionisis Margaris

引用次数: 3

Travel Routes Recommendations via Online Social Networks 通过在线社交网络推荐旅游路线

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3345619

C. Comito

引用次数: 4

Social Media as a Main Source of Customer Feedback - Alternative to Customer Satisfaction Surveys 社交媒体作为客户反馈的主要来源——替代客户满意度调查

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3345642

S. Hasson, J. Piorkowski, I. McCulloh

引用次数: 6

RumorSleuth: Joint Detection of Rumor Veracity and User Stance 谣言侦探:谣言真实性和用户立场的联合检测

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342916

Mohammad Raihanul Islam, S. Muthiah, Naren Ramakrishnan

{"title":"RumorSleuth: Joint Detection of Rumor Veracity and User Stance","authors":"Mohammad Raihanul Islam, S. Muthiah, Naren Ramakrishnan","doi":"10.1145/3341161.3342916","DOIUrl":"https://doi.org/10.1145/3341161.3342916","url":null,"abstract":"The penetration of social media has had deep and far-reaching consequences in information production and consumption. Widespread use of social media platforms has engendered malicious users and attention seekers to spread rumors and fake news. This trend is particularly evident in various microblogging platforms where news becomes viral in a matter of hours and can lead to mass panic and confusion. One intriguing fact regarding rumors and fake news is that very often rumor stories prompt users to adopt different stances about the rumor posts. Understanding user stances in rumor posts is thus very important to identify the veracity of the underlying content. While rumor veracity and stance detection have been viewed as disjoint tasks we demonstrate here how jointly learning both of them can be fruitful. In this paper, we propose RumorSleuth, a multitask deep learning model which can leverage both the textual information and user profile information to jointly identify the veracity of a rumor along with users' stances. Tests on two publicly available rumor datasets demonstrate that RumorSleuth outperforms current state-of-the-art models and achieves up to 14% performance gain in rumor veracity classification and around 6% improvement in user stance classification.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123946996","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: 21

Vertex-Weighted Measures for Link Prediction in Hashtag Graphs 标签图中链接预测的顶点加权测度

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3344828

Logan Praznik, Gautam Srivastava, Chetan Mendhe, Vijay K. Mago

{"title":"Vertex-Weighted Measures for Link Prediction in Hashtag Graphs","authors":"Logan Praznik, Gautam Srivastava, Chetan Mendhe, Vijay K. Mago","doi":"10.1145/3341161.3344828","DOIUrl":"https://doi.org/10.1145/3341161.3344828","url":null,"abstract":"Communications on the popular social networking platform, Twitter, can be mapped in terms of a hashtag graph, where vertices correspond to hashtags, and edges correspond to co-occurrences of hashtags within the same distinct tweet. Furthermore, a vertex in hashtag graphs can be weighted with the number of tweets a hashtag has occurred in, and edges can be weighted with the number of tweets both hashtags have co-occurred in. In this paper, we describe additions to some well-known link prediction methods that allow the weights of both vertices and edges in a weighted hashtag graph to be taken into account. We base our novel predictive additions on the assumption that more popular hashtags have a higher probability to appear with other hashtags in the future. We then apply these improved methods to 3 sets of Twitter data with the intent of predicting hashtags co-occurences in the future. Experimental results on real-life data sets consisting of over 3, 000, 000 combined unique Tweets and over 250, 000 unique hashtags show the effectiveness of the proposed models and algorithms on weighted hashtag graphs.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130981635","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: 9

Fast Incremental Computation of Harmonic Closeness Centrality in Directed Weighted Networks 有向加权网络中谐波密切度的快速增量计算

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3344829

K. Putman, Hanjo D. Boekhout, Frank W. Takes

{"title":"Fast Incremental Computation of Harmonic Closeness Centrality in Directed Weighted Networks","authors":"K. Putman, Hanjo D. Boekhout, Frank W. Takes","doi":"10.1145/3341161.3344829","DOIUrl":"https://doi.org/10.1145/3341161.3344829","url":null,"abstract":"This paper proposes a novel approach to efficiently compute the exact closeness centrality values of all nodes in dynamically evolving directed and weighted networks. Closeness centrality is one of the most frequently used centrality measures in the field of social network analysis. It uses the total distance to all other nodes to determine node centrality. Previous work has addressed the problem of dynamically updating closeness centrality values for either undirected networks or only for the top-$k$ nodes in terms of closeness centrality. Here, we propose a fast approach for exactly computing all closeness centrality values at each timestamp of directed and weighted evolving networks. Such networks are prevalent in many real-world situations. The main ingredients of our approach are a combination of work filtering methods and efficient incremental updates that avoid unnecessary recomputation. We tested the approach on several real-world datasets of dynamic small-world networks and found that we have mean speed-ups of about 33 times. In addition, the method is highly parallelizable.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125307799","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

Two Decades of Network Science: as seen through the co-authorship network of network scientists 网络科学的二十年:通过网络科学家的合著者网络看到的

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343685

Roland Molontay, Marcell Nagy

{"title":"Two Decades of Network Science: as seen through the co-authorship network of network scientists","authors":"Roland Molontay, Marcell Nagy","doi":"10.1145/3341161.3343685","DOIUrl":"https://doi.org/10.1145/3341161.3343685","url":null,"abstract":"Complex networks have attracted a great deal of research interest in the last two decades since Watts & Strogatz, Barabási & Albert and Girvan & Newman published their highly-cited seminal papers on small-world networks, on scale-free networks and on the community structure of complex networks, respectively. These fundamental papers initiated a new era of research establishing an interdisciplinary field called network science. Due to the multidisciplinary nature of the field, a diverse but not divided network science community has emerged in the past 20 years. This paper honors the contributions of network science by exploring the evolution of this community as seen through the growing co-authorship network of network scientists (here the notion refers to a scholar with at least one paper citing at least one of the three aforementioned milestone papers). After investigating various characteristics of 29,528 network science papers, we construct the co-authorship network of 52,406 network scientists and we analyze its topology and dynamics. We shed light on the collaboration patterns of the last 20 years of network science by investigating numerous structural properties of the co-authorship network and by using enhanced data visualization techniques. We also identify the most central authors, the largest communities, investigate the spatiotemporal changes, and compare the properties of the network to scientometric indicators.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114444137","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: 21

Next Cashtag Prediction on Social Trading Platforms with Auxiliary Tasks 基于辅助任务的社交交易平台下一个现金标签预测

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342945

Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen

引用次数: 7

Strengthening Social Networks Analysis by Networks Fusion 利用网络融合加强社会网络分析

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342939

Feiyu Long, Nianwen Ning, Chenguang Song, Bin Wu

引用次数: 3