2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第6页

Network Sparsification via Degree- and Subgraph-based Edge Sampling 基于度和子图边缘采样的网络稀疏化

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068651

Zhen Su, Jürgen Kurths, Henning Meyerhenke

{"title":"Network Sparsification via Degree- and Subgraph-based Edge Sampling","authors":"Zhen Su, Jürgen Kurths, Henning Meyerhenke","doi":"10.1109/ASONAM55673.2022.10068651","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068651","url":null,"abstract":"Network (or graph) sparsification compresses a graph by removing inessential edges. By reducing the data volume, it accelerates or even facilitates many downstream analyses. Still, the accuracy of many sparsification methods, with filtering-based edge sampling being the most typical one, heavily relies on an appropriate definition of edge importance. Instead, we propose a different perspective with a generalized local-property-based sampling method, which preserves (scaled) local node characteristics. Apart from degrees, these local node characteristics we use are the expected (scaled) number of wedges and triangles a node belongs to. Through such a preservation, main complex structural properties are preserved implicitly. We adapt a game-theoretic framework from uncertain graph sampling by including a threshold for faster convergence (at least 4 times faster empirically) to approximate solutions. Extensive experimental studies on functional climate networks show the effectiveness of this method in preserving macroscopic to meso-scopic and microscopic network structural properties.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122799491","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

Exploring Mental Health Communications among Instagram Coaches 探索Instagram教练之间的心理健康交流

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068611

E. Haq, Lik-Hang Lee, Gareth Tyson, Reza Hadi Mogavi, Tristan Braud, Pan Hui

引用次数: 0

NetDriller-V3: A Powerful Social Network Analysis Tool netdrill - v3:一个强大的社会网络分析工具

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068570

Salim Afra, Tansel Özyer, J. Rokne, R. Alhajj

{"title":"NetDriller-V3: A Powerful Social Network Analysis Tool","authors":"Salim Afra, Tansel Özyer, J. Rokne, R. Alhajj","doi":"10.1109/ASONAM55673.2022.10068570","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068570","url":null,"abstract":"The development in technology has led to the generation of huge amounts of data from various sources, including biological data, social networking data, etc. Accordingly, social network analysis has received considerable attention with the availability of more raw datasets which could be realized using a network structure. Most of the datasets can be represented as a social network which is a graph consisting of actors having relationships. Many tools exist for social network analysis inspired to extract knowledge from the networks. NetDriller has been developed as a social network extraction, manipulation and analysis tool to cover the lack that exists in other tools. It is capable of constructing social networks from raw data by employing a variety of data mining and machine learning techniques. In this paper, we describe an extend version of NetDriller, which has some new essential functions, including social network construction using data collection from Twitter, DBLP and IEEE. We also added (1) a new chart for viewing the network property and metrics, and (2) new graph manipulation techniques using GUI to keep the tool up to date with the huge volume of networks and the different types of raw data available on the web.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115186549","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

A Time-Dependent-Based Approach to Enhance Self-Harm Prediction 基于时间依赖的自我伤害预测方法

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068572

Etienne Gael Tajeuna, M. Bouguessa

引用次数: 0

ASONAM 2022 Keynotes

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/asonam55673.2022.10068683

引用次数: 0

Minimizing the Importance Inequality of Nodes in a Social Network Graph 最小化社交网络图中节点的重要性不等式

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068586

A. Zareie, R. Sakellariou

{"title":"Minimizing the Importance Inequality of Nodes in a Social Network Graph","authors":"A. Zareie, R. Sakellariou","doi":"10.1109/ASONAM55673.2022.10068586","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068586","url":null,"abstract":"Network graphs are widely used to model a variety of real-world interactions. In such graphs, nodes do not have the same importance in the graph structure as a result of the graph's topological properties. This may have various implications concerning a network's behaviour as, for example, how different nodes operate (even a node's failure) may not have the same impact for the whole network. The differences in the structural properties of the nodes imply that each node has different importance, which, in turn, gives rise to the notion of importance inequality in a graph. This paper defines and addresses the problem of importance inequality minimization, which may be useful to achieve certain properties in a network. Given a network graph and an integer $k$, the problem aims to identify $k$ edges to connect non-adjacent nodes, in a way that minimizes the importance inequality of the graph. The paper provides a formal definition of the problem and proves its NP-hardness. Then, a naive greedy method is proposed, which is enhanced by heuristics that make its use practical. Experiments using 8 real-world networks are conducted to evaluate the proposed methods in terms of effectiveness and efficiency.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125416104","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

Wheats the Deal? Understanding the GMO debate in online forums 小麦的交易?了解在线论坛上的转基因争论

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068702

Arman Irani, K. Esterling, M. Faloutsos, D. Pagliaccia

{"title":"Wheats the Deal? Understanding the GMO debate in online forums","authors":"Arman Irani, K. Esterling, M. Faloutsos, D. Pagliaccia","doi":"10.1109/ASONAM55673.2022.10068702","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068702","url":null,"abstract":"How can we comprehensively understand the main concerns and beliefs of the GMO debate in online forums? Genetically Modified Organisms (GMOs) have historically been a hotly debated topic, both within and outside of the agriculture industry. Understanding the complexity of these beliefs can lend policy makers the knowledge necessary to counteract misinformation. In this paper we develop Forumlyze, a systematic framework to understand user beliefs in online discourse surrounding an issue. As a case study, we focus on data collected from Reddit between 2019–2020 from four sub-forums: farming, agriculture, horticulture, and vegetable gardening. In our approach we (a) illustrate the fundamental and temporal characteristics of the issue (b) extract and characterize sentiments surrounding the issue (c) uncover the dominate concepts prevalent in this discussion and the context surrounding these concepts. The comprehensive nature of this analysis led to the following results. (1) The dominant concepts surrounding GMOs are Climate Change, Monsanto and Soil Science. (2) The sentiment of discourse around GMOs and its related concepts indicates a polarized affective system. (3) Evidence that real-world events impact online forum communities' sentiment surrounding GMOs-related concepts.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131812555","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

The Pursuit of Being Heard: An Unsupervised Approach to Narrative Detection in Online Protest 对被倾听的追求:网络抗议叙事检测的一种无监督方法

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068671

Kumari Neha, Vibhu Agrawal, Arun Balaji Buduru, P. Kumaraguru

{"title":"The Pursuit of Being Heard: An Unsupervised Approach to Narrative Detection in Online Protest","authors":"Kumari Neha, Vibhu Agrawal, Arun Balaji Buduru, P. Kumaraguru","doi":"10.1109/ASONAM55673.2022.10068671","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068671","url":null,"abstract":"Protests and mass mobilization are scarce; however, they may lead to dramatic outcomes when they occur. Social media such as Twitter has become a center point for the organization and development of online protests worldwide. It becomes crucial to decipher various narratives shared during an online protest to understand people's perceptions. In this work, we propose an unsupervised clustering-based framework to understand the narratives present in a given online protest. Through a comparative analysis of tweet clusters in 3 protests around government policy bills, we contribute novel insights about narratives shared during an online protest. Across case studies of government policy-induced online protests in India and the United Kingdom, we found familiar mass mo-bilization narratives across protests. We found reports of on-ground activities and call-to-action for people's participation narrative clusters in all three protests under study. We also found protest-centric narratives in different protests, such as skepticism around the topic. The results from our analysis can be used to understand and compare people's perceptions of future mass mobilizations.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114342728","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

ASONAM 2022 Tutorial I: Mining and Analysing Collaboration in git Repositories with git2net ASONAM 2022教程1:使用git2net挖掘和分析git存储库中的协作

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/asonam55673.2022.10068714

Christoph Gote

{"title":"ASONAM 2022 Tutorial I: Mining and Analysing Collaboration in git Repositories with git2net","authors":"Christoph Gote","doi":"10.1109/asonam55673.2022.10068714","DOIUrl":"https://doi.org/10.1109/asonam55673.2022.10068714","url":null,"abstract":"The tutorial will provide an introduction to the git2net, an open-source Python package for mining and analyzing collaboration in git repositories. The tutorial will cover various aspects of using git2net to analyze collaboration within git repositories, including hands-on examples and interactive tutorials using Jupyter notebooks. Attendees will learn how to use git2net to extract co-editing networks, visualize collaboration patterns, and analyze the contributions of individual developers. The tutorial is platform-independent and can be used on all platforms. The tutorial materials, including Jupyter notebooks, can be accessed through the git repository (https://github.com/gotec/git2net-tutorials). Attendees can directly interact with the notebooks through Binder, Google Colab or view them in NBViewer by following the links provided in the abstract. The tutorial will cover topics such as cloning a repository for analysis, mining git repositories with git2net, author disambiguation with gambit, network analysis with pathpy, database-based analyses, and computing file complexity with git2net. Installation instructions for git2net as well as all other information regarding its development can be found in the original development repository (https://github.com/gotec/git2net). The tutorial is suitable for developers, data scientists and researchers who are interested in understanding collaboration patterns in software development.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114351973","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

Identifying Power Elites in Massively Multiplayer Online Games by Applying Machine Learning to Communication and Support Networks 通过将机器学习应用于交流和支持网络来识别大型多人在线游戏中的权力精英

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068676

S. Müller, Raji Ghawi, Jürgen Pfeffer

{"title":"Identifying Power Elites in Massively Multiplayer Online Games by Applying Machine Learning to Communication and Support Networks","authors":"S. Müller, Raji Ghawi, Jürgen Pfeffer","doi":"10.1109/ASONAM55673.2022.10068676","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068676","url":null,"abstract":"The aim of this paper is to show how machine learning can predict whether an individual is more powerful than others in the group. The crucial point here is to consider the structural position of the actors in the social networks in which they are embedded. The approach we have taken for constructing these intra-group networks is the aggregation of communication and support interactions. Our research is based on longitutional data from the Massively Multiplayer Online Game (MMOG) Travian that was collected over a 12-month period. The data includes 202,764 communication and 96,913 support interactions between players that we applied for the construction of interaction networks. We also had access to status information on a daily basis for 21,431 individual players who were members of 4,758 alliances. Methodically, we applied 10 established metrics from SNA-based team research in combination with the Random Forstest classification algorithm. Our results show that interaction networks are well suited to assign members into two groups of powerful (elite) and nonpowerful (non-elite) players. It turned out that the identification of non-elite members was much easier to accomplish than that of elite members. Regarding the application of multiplex networks, we could not confirm a higher explanatory power by using combined networks. In summary, we can say that the network patterns of elite members are clearly different from those of non-elite members. In this way, we were able to predict affiliation to each category with an accuracy (F1) of 0.88 for communication networks and 0.83 for support networks.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124085471","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