Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining最新文献

Characterizing video-based online information environment using VTracker 利用VTracker表征基于视频的在线信息环境

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3489480

Thomas Marcoux, Oluwaseyi Adeliyi, Dr Nidhi Agarwal

引用次数: 0

Fast indexing algorithm for efficient kNN queries on complex networks 复杂网络中高效kNN查询的快速索引算法

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3489442

Suomi Kobayashi, Shohei Matsugu, Hiroaki Shiokawa

引用次数: 2

Temporalizing static graph autoencoders to handle temporal networks 时序化静态图形自动编码器来处理时序网络

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488333

Mounir Haddad, Cécile Bothorel, P. Lenca, Dominique Bedart

引用次数: 1

The stock exchange of influencers: a financial approach for studying fanbase variation trends 影响者的股票交易:研究粉丝基础变化趋势的财务方法

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488413

Fabio Bertone, L. Vassio, Martino Trevisan

{"title":"The stock exchange of influencers: a financial approach for studying fanbase variation trends","authors":"Fabio Bertone, L. Vassio, Martino Trevisan","doi":"10.1145/3487351.3488413","DOIUrl":"https://doi.org/10.1145/3487351.3488413","url":null,"abstract":"In many online social networks (OSNs), a limited portion of profiles emerges and reaches a large base of followers, i.e., the so-called social influencers. One of their main goals is to increase their fanbase to increase their visibility, engaging users through their content. In this work, we propose a novel parallel between the ecosystem of OSNs and the stock exchange market. Followers act as private investors, and they follow influencers, i.e., buy stocks, based on their individual preferences and on the information they gather through external sources. In this preliminary study, we show how the approaches proposed in the context of the stock exchange market can be successfully applied to social networks. Our case study focuses on 60 Italian Instagram influencers and shows how their followers short-term trends obtained through Bollinger bands become close to those found in external sources, Google Trends in our case, similarly to phenomena already observed in the financial market. Besides providing a strong correlation between these different trends, our results pose the basis for studying social networks with a new lens, linking them with a different domain.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125982271","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

Forward and backward linear threshold ranks 正向和反向线性阈值排序

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488355

M. Blesa, Pau García-Rodríguez, M. Serna

引用次数: 5

Peeking through the homelessness system with a network science lens 从网络科学的角度透视无家可归者系统

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488321

C. Chelmis, Khandker Sadia Rahman

引用次数: 3

A mathematical model for friend discovery from dynamic social graphs 动态社交图谱中朋友发现的数学模型

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3489473

C. Leung, S. Singh

引用次数: 4

Knowledge graph based platform of COVID-19 drugs and symptoms 基于知识图谱的新型冠状病毒药物与症状研究平台

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3489484

Zhenhe Pan, Shuang Jiang, Juntao Su, Muzhe Guo, Yuanlin Zhang

{"title":"Knowledge graph based platform of COVID-19 drugs and symptoms","authors":"Zhenhe Pan, Shuang Jiang, Juntao Su, Muzhe Guo, Yuanlin Zhang","doi":"10.1145/3487351.3489484","DOIUrl":"https://doi.org/10.1145/3487351.3489484","url":null,"abstract":"Since the first cased of COVID-19 was identified in December 2019, a plethora of different drugs have been tested for COVID-19 treatment, making it a daunting task to keep track of the rapid growth of COVID-19 research landscape. Using the existing scientific literature search systems to develop a deeper understanding of COVID-19 related clinical experiments and results turns to be increasingly complicated. In this paper, we build a named entity recognition-based framework to extract information accurately and generate knowledge graph efficiently from a myriad of clinical test results articles. Of the tested drugs to treat COVID-19, we also develop a question answering system answers to medical questions regarding COVID-19 related symptoms using Wikipedia articles. We combine the state-of-the-art question answering model - Bidirectional Encoder Representations from Transformers (BERT), with Knowledge Graph to answer patients' questions about treatment options for their symptoms. This generated knowledge graph is user-friendly with intuitive and convenient tools to find the supporting and/or contradictory references of certain drugs with properties such as side effects, target population, etc. The trained question answering platform provides a straightforward and error-tolerant way to query for treatment suggestions given uses' input symptoms.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130688992","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: 0

Detecting cyber security related Twitter accounts and different sub-groups: a multi-classifier approach 检测与网络安全相关的Twitter账户和不同的子组:一种多分类方法

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3492716

Mohamad Imad Mahaini, Shujun Li

{"title":"Detecting cyber security related Twitter accounts and different sub-groups: a multi-classifier approach","authors":"Mohamad Imad Mahaini, Shujun Li","doi":"10.1145/3487351.3492716","DOIUrl":"https://doi.org/10.1145/3487351.3492716","url":null,"abstract":"Many cyber security experts, organizations, and cyber criminals are active users on online social networks (OSNs). Therefore, detecting cyber security related accounts on OSNs and monitoring their activities can be very useful for different purposes such as cyber threat intelligence, detecting and preventing cyber attacks and online harms on OSNs, and evaluating the effectiveness of cyber security awareness activities on OSNs. In this paper, we report our work on developing several machine learning based classifiers for detecting cyber security related accounts on Twitter, including a base-line classifier for detecting cyber security related accounts in general, and three sub-classifiers for detecting three subsets of cyber security related accounts (individuals, hackers, and academia). To train and test the classifiers, we followed a more systemic approach (based on a cyber security taxonomy, real-time sampling of tweets, and crowdsourcing) to construct a dataset of cyber security related accounts with multiple tags assigned to each account. For each classifier, we considered a richer set of features than those used in past studies. Among five machine learning models tested, the Random Forest model achieved the best performance: 93% for the baseline classifier, 88-91% for the three sub-classifiers. We also studied feature reduction of the base-line classifier and showed that using just six features we can already achieve the same performance.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115541638","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

An insight into network structure measures and number of driver nodes 对网络结构、度量和驱动节点数量的洞察

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488557

Abida Sadaf, Luke Mathieson, Katarzyna Musial

{"title":"An insight into network structure measures and number of driver nodes","authors":"Abida Sadaf, Luke Mathieson, Katarzyna Musial","doi":"10.1145/3487351.3488557","DOIUrl":"https://doi.org/10.1145/3487351.3488557","url":null,"abstract":"Control of complex networks is one of the most challenging open problems within network science. One view says that we can only claim to fully understand a network if we have the ability to influence or control it and predict the results of the employed control mechanisms. The area of control and controllability has progressed notably in the past ten years with several frameworks proposed namely, structural, exact, and physical. With continuing advancement in the area, the need to develop effective and efficient control methods that provide robust control is increasingly critical. The ultimate responsibility for controlling the network lies with the set of driver nodes that, according to the classical definition of the control theory of complex systems, can steer the network from any given state to a desired final state. To be able to develop better control mechanisms, we need to understand the relationship between different network structures and the number of driver nodes needed to control a given structure. This will allow understanding of which networks might be easier to control and the resources needed to control them. In this paper, we present a systematic study that builds an understanding of how network profiles (random (R), small-world (SW), scale-free (SF)) influence the number of driver nodes needed for control. Additionally, we also consider real social networks and identify their driver nodes set to further expand the discussion. We mean to find a correlation between network structure measures and number of driver nodes. Our results show that there is in fact a strong relationship between these.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"402 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114887428","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