2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第6页

Flexible Compression of Big Data 灵活压缩大数据

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343512

C. Leung, Fan Jiang, Yibin Zhang

{"title":"Flexible Compression of Big Data","authors":"C. Leung, Fan Jiang, Yibin Zhang","doi":"10.1145/3341161.3343512","DOIUrl":"https://doi.org/10.1145/3341161.3343512","url":null,"abstract":"High volumes of valuable data and information can be easily collected in the current era of big data. As rich and constant sources of big data, an incredible amount of people from different social stratum take part in social networks. Hence, social networks are desired for many research topics. In social networks, users (or social entities) are often linked by some ‘following’ relationships. As the social networks growing, some famous users account (or social entities) might be followed by a large number of same other users. In this situation, we call those famous users as frequently followed groups, which some researchers (or businesses) may be interested in them for investigating. However, the discovery of those frequently followed groups might be difficult and challenging because the following data in social networks are usually very big but sparse (huge number of users lead to big ‘following’ data, but each user is likely only following a small number of other users). As a result, in this paper, we present a new compression model, which can be used during mining these very big but sparse social networks for discovering the frequently followed groups of users/social entities.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"26 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":"134383132","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: 5

Dynamic Consensus Community Detection and Combinatorial Multi-Armed Bandit 动态共识社区检测与组合多臂强盗

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342910

Domenico Mandaglio, Andrea Tagarelli

{"title":"Dynamic Consensus Community Detection and Combinatorial Multi-Armed Bandit","authors":"Domenico Mandaglio, Andrea Tagarelli","doi":"10.1145/3341161.3342910","DOIUrl":"https://doi.org/10.1145/3341161.3342910","url":null,"abstract":"Community detection and evolution has been largely studied in the last few years, especially for network systems that are inherently dynamic and undergo different types of changes in their structure and organization in communities. Because of the inherent uncertainty and dynamicity in such network systems, we argue that temporal community detection problems can profitably be solved under a particular class of multi-armed bandit problems, namely combinatorial multi-armed bandit (CMAB). More specifically, we propose a CMAB-based methodology for the novel problem of dynamic consensus community detection, i.e., to compute a single community structure that is designed to encompass the whole information available in the sequence of observed temporal snapshots of a network in order to be representative of the knowledge available from community structures at the different time steps. Unlike existing approaches, our key idea is to produce a dynamic consensus solution for a temporal network to have unique capability of embedding both long-term changes in the community formation and newly observed community structures.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"43 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":"132364331","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: 3

A Model Based on Random Walk with Restart to Predict CircRNA - Disease Associations on Heterogeneous Network 基于随机行走与重启的异质网络CircRNA -疾病关联预测模型

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343514

H. Vural, Mehmet Kaya, R. Alhajj

引用次数: 9

Impact of Social Influence on Adoption Behavior: An Online Controlled Experimental Evaluation 社会影响对收养行为的影响:一项在线控制实验评估

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342882

Soumajyoti Sarkar, Ashkan Aleali, P. Shakarian, Mika Armenta, Danielle Sanchez, K. Lakkaraju

{"title":"Impact of Social Influence on Adoption Behavior: An Online Controlled Experimental Evaluation","authors":"Soumajyoti Sarkar, Ashkan Aleali, P. Shakarian, Mika Armenta, Danielle Sanchez, K. Lakkaraju","doi":"10.1145/3341161.3342882","DOIUrl":"https://doi.org/10.1145/3341161.3342882","url":null,"abstract":"It is widely believed that the adoption behavior of a decision-maker in a social network is related to the number of signals it receives from its peers in the social network. It is unclear if these same principles hold when the “pattern” by which they receive these signals vary and when potential decisions have different utilities. To investigate that, we manipulate social signal exposure in an online controlled experiment with human participants. Specifically, we change the number of signals and the pattern through which participants receive them over time. We analyze its effect through a controlled game where each participant makes a decision to select one option when presented with six choices with differing utilities, with one choice having the most utility. We avoided network effects by holding the neighborhood network of the users constant. Over multiple rounds of the game, we observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the six choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar choices, (2) when the quantity of social signals vary over time, the probability that a participant selects the decision similar to the one reflected by the social signals and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals and (3) an early subjugation to higher quantity of peer social signals turned out to be a more effective strategy of social influence when aggregated over the rounds.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"2 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":"129395076","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: 3

A Postmortem of Suspended Twitter Accounts in the 2016 U.S. Presidential Election 2016年美国总统大选中被暂停的推特账户的事后分析

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342878

Huyen T. Le, G. Boynton, Zubair Shafiq, P. Srinivasan

{"title":"A Postmortem of Suspended Twitter Accounts in the 2016 U.S. Presidential Election","authors":"Huyen T. Le, G. Boynton, Zubair Shafiq, P. Srinivasan","doi":"10.1145/3341161.3342878","DOIUrl":"https://doi.org/10.1145/3341161.3342878","url":null,"abstract":"Social media sites such as Twitter have faced significant pressure to mitigate spam and abuse on their platform in the aftermath of congressional investigations into Russian interference in the 2016 U.S. presidential election. Twitter publicly acknowledged the exploitation of their platform and has since conducted aggressive cleanups to suspend the involved accounts. To shed light on Twitter's countermeasures, we conduct a postmortem analysis of about one million Twitter accounts who engaged in the 2016 U.S. presidential election but were later suspended by Twitter. To systematically analyze coordinated activities of these suspended accounts, we group them into communities based on their retweet/mention network and analyze different characteristics such as popular tweeters, domains, and hashtags. The results show that suspended and regular communities exhibit significant differences in terms of popular tweeter and hashtags. Our qualitative analysis also shows that suspended communities are heterogeneous in terms of their characteristics. We further find that accounts suspended by Twitter's new countermeasures are tightly connected to the original suspended communities.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"26 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":"134030956","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: 15

Dormant Bots in Social Media: Twitter and the 2018 U.S. Senate Election 社交媒体中的休眠机器人:推特和2018年美国参议院选举

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343852

Richard Takacs, I. McCulloh

{"title":"Dormant Bots in Social Media: Twitter and the 2018 U.S. Senate Election","authors":"Richard Takacs, I. McCulloh","doi":"10.1145/3341161.3343852","DOIUrl":"https://doi.org/10.1145/3341161.3343852","url":null,"abstract":"Bots are often identified on social media due to their behavior. How easily are they identified, however, when they are dormant and exhibit no measurable behavior at all, except for their silence? We identified “dormant bot networks” positioned to influence social media discourse surrounding the 2018 U.S. senate election. A dormant bot is a social media persona that does not post content yet has large follower and friend relationships with other users. These relationships may be used to manipulate online narratives and elevate or suppress certain discussions in the social media feed of users. Using a simple structure-based approach, we identify a large number of dormant bots created in 2017 that begin following the social media accounts of numerous US government politicians running for re-election in 2018. Findings from this research were used by the U.S. Government to suspend dormant bots prior to the elections to prevent any malign influence campaign. Application of this approach by social media providers may provide a novel method to reduce the risk of content manipulation for online platforms.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"40 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":"133635469","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

Understanding Information Operations using YouTubeTracker 了解信息使用YouTubeTracker的操作

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343704

Thomas Marcoux, Nitin Agarwal, A. Obadimu, Muhammad Nihal Hussain, K. Galeano, Samer Al-khateeb

{"title":"Understanding Information Operations using YouTubeTracker","authors":"Thomas Marcoux, Nitin Agarwal, A. Obadimu, Muhammad Nihal Hussain, K. Galeano, Samer Al-khateeb","doi":"10.1145/3341161.3343704","DOIUrl":"https://doi.org/10.1145/3341161.3343704","url":null,"abstract":"YouTube is the second most popular website in the world. Over 300 hours worth of videos are uploaded every minute and 5 billion videos are watched every day - almost one video per person worldwide. Because videos can deliver a complex message in a way that captures the audience's attention more effectively than text-based platforms, it has become one of the most relevant platforms in the age of digital mass communication. This makes the analysis of YouTube content and user behavior invaluable not only to information scientists but also communication researchers, journalists, sociologists, and many more. There exists a number of YouTube analysis tools but none of them provide an in-depth qualitative and quantitative insights into user behavior or networks. Towards that direction, we introduce YouTubeTracker - a tool designed to gather YouTube data and gain insights on content and users. This tool can help identify leading actors, networks and spheres of influence, emerging popular trends, as well as user opinion. This analysis can also be used to understand user engagement and social networks. This can help reveal suspicious and inorganic behaviors (e.g., trolling, botting) causing algorithmic manipulations.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"23 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":"132441127","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: 4

Becoming Gatekeepers Together with Allies: Collaborative Brokerage over Social Networks 与盟友一起成为看门人:社交网络上的协作经纪

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342874

Yang Chen, Jiamou Liu

引用次数: 2

Epidemic Threshold and Lifetime Distribution for Information Diffusion on Simultaneously Growing Networks 同步增长网络上信息扩散的流行阈值和寿命分布

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342891

Emily M. Fischer, Souvik Ghosh, G. Samorodnitsky

引用次数: 3

Reducing Features to Improve Link Prediction Performance in Location Based Social Networks, Non-Monotonically Selected Subset from Feature Clusters 在基于位置的社交网络中减少特征以提高链接预测性能，从特征簇中非单调选择子集

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343853

A. Bayrak, Faruk Polat

引用次数: 0