Anjan Chowdhury, S. Srinivasan, S. Bhowmick, Animesh Mukherjee, K. Ghosh
Constant communities, i.e., groups of vertices that are always clustered together, independent of the community detection algorithm used, are necessary for reducing the inherent stochasticity of community detection results. Current methods for identifying constant communities require multiple runs of community detection algorithm(s). This process is extremely time consuming and not scalable to large networks. We propose a novel approach for finding the constant communities, by transforming the problem to a binary classification of edges. We apply the Otsu method from image thresholding to classify edges based on whether they are always within a community or not. Our algorithm does not require any explicit detection of communities and can thus scale to very large networks of the order of millions of vertices. Our results on real-world graphs show that our method is significantly faster and the constant communities produced have higher accuracy (as per F1 and NMI scores) than state-of-the-art baseline methods.
{"title":"Constant community identification in million scale networks using image thresholding algorithms","authors":"Anjan Chowdhury, S. Srinivasan, S. Bhowmick, Animesh Mukherjee, K. Ghosh","doi":"10.1145/3487351.3488350","DOIUrl":"https://doi.org/10.1145/3487351.3488350","url":null,"abstract":"Constant communities, i.e., groups of vertices that are always clustered together, independent of the community detection algorithm used, are necessary for reducing the inherent stochasticity of community detection results. Current methods for identifying constant communities require multiple runs of community detection algorithm(s). This process is extremely time consuming and not scalable to large networks. We propose a novel approach for finding the constant communities, by transforming the problem to a binary classification of edges. We apply the Otsu method from image thresholding to classify edges based on whether they are always within a community or not. Our algorithm does not require any explicit detection of communities and can thus scale to very large networks of the order of millions of vertices. Our results on real-world graphs show that our method is significantly faster and the constant communities produced have higher accuracy (as per F1 and NMI scores) than state-of-the-art baseline methods.","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":"126757708","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}
Cryptocurrency anti-money laundering has become an important research topic in recent years. Legal empirical research combined with AI technology has received considerable attention. How to construct a knowledge graph of cryptocurrency anti-money laundering in a small sample of international cases and judgments on the prevention and control of cryptocurrency money laundering has become an essential issue for a better understanding of the relationship between the crime patterns and emerging financial technologies. In this study, we proposed artificial intelligence meta-learning with a few-shot learning model to construct a cryptocurrency anti-money laundering knowledge graph. The research method of this study aims at the abuse of electronic payment tools and cryptocurrency in various crimes by analyzing the causes and background, the amount of money, the type of crime, and the growth trend in recent years. The contribution of this study is that the proposed AI cryptocurrency anti-money laundering knowledge graphs in fintech can be applied to the content analysis and question-and-answer system of legal documents.
{"title":"Artificial intelligence for knowledge graphs of cryptocurrency anti-money laundering in fintech","authors":"Min-Yuh Day","doi":"10.1145/3487351.3488415","DOIUrl":"https://doi.org/10.1145/3487351.3488415","url":null,"abstract":"Cryptocurrency anti-money laundering has become an important research topic in recent years. Legal empirical research combined with AI technology has received considerable attention. How to construct a knowledge graph of cryptocurrency anti-money laundering in a small sample of international cases and judgments on the prevention and control of cryptocurrency money laundering has become an essential issue for a better understanding of the relationship between the crime patterns and emerging financial technologies. In this study, we proposed artificial intelligence meta-learning with a few-shot learning model to construct a cryptocurrency anti-money laundering knowledge graph. The research method of this study aims at the abuse of electronic payment tools and cryptocurrency in various crimes by analyzing the causes and background, the amount of money, the type of crime, and the growth trend in recent years. The contribution of this study is that the proposed AI cryptocurrency anti-money laundering knowledge graphs in fintech can be applied to the content analysis and question-and-answer system of legal documents.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"13 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":"127732888","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}
Social network-based team formation problem has been widely studied from different aspects. However, the skills in earlier works were treated equally, and cannot be substituted by other related or similar skills. In addition, assigning experts who possess alternative skills for a required skill is not allowed. To better fit real world scenarios, we propose a novel hierarchical skill model to let skills interchangeable. By considering the communication cost and the personnel cost, we develop an optimization framework under the hierarchical skill model to deal with the trade-off between communication and personnel cost. The experiments show that our proposed framework and the hierarchical skill model is reasonable and has better performance than earlier works.
{"title":"Forming a team of cost-effective and well-collaborated experts in social networks based on hierarchical skill model","authors":"Fa-Yuan Liu, Shiou-Chi Li, Jen-Wei Huang","doi":"10.1145/3487351.3488359","DOIUrl":"https://doi.org/10.1145/3487351.3488359","url":null,"abstract":"Social network-based team formation problem has been widely studied from different aspects. However, the skills in earlier works were treated equally, and cannot be substituted by other related or similar skills. In addition, assigning experts who possess alternative skills for a required skill is not allowed. To better fit real world scenarios, we propose a novel hierarchical skill model to let skills interchangeable. By considering the communication cost and the personnel cost, we develop an optimization framework under the hierarchical skill model to deal with the trade-off between communication and personnel cost. The experiments show that our proposed framework and the hierarchical skill model is reasonable and has better performance than earlier works.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"12 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":"121781075","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}
Matching the anonymous profile of an individual in an online social network (OSN) to their real identity raises serious privacy concerns as one can obtain sensitive information about that individual. Previous work has formulated the profile matching risk in several different ways and has shown that there exists a non-negligible risk of matching user profiles across OSNs. However, they are not practical to convey the risk to OSN users in real-time. In this work, using the output of such formulation, we model the profile characteristics of users that are vulnerable to profile matching via machine learning and make probabilistic inferences about how the vulnerabilities of users change as they share new content in OSNs (or as their graph connectivity changes). We evaluate the generated models in real data. Our results show that the generated models determine with high accuracy whether a user profile is vulnerable to profile matching risk by only analyzing their publicly available information in the anonymous OSN. In addition, we develop optimization-based countermeasures to preserve the user's privacy as they share their OSN profile with third parties. We believe that this work will be crucial for OSN users to understand their privacy risks due to their public sharings and be more conscious about their online privacy.
{"title":"Real-time privacy risk quantification in online social networks","authors":"Anisa Halimi, Erman Ayday","doi":"10.1145/3487351.3488272","DOIUrl":"https://doi.org/10.1145/3487351.3488272","url":null,"abstract":"Matching the anonymous profile of an individual in an online social network (OSN) to their real identity raises serious privacy concerns as one can obtain sensitive information about that individual. Previous work has formulated the profile matching risk in several different ways and has shown that there exists a non-negligible risk of matching user profiles across OSNs. However, they are not practical to convey the risk to OSN users in real-time. In this work, using the output of such formulation, we model the profile characteristics of users that are vulnerable to profile matching via machine learning and make probabilistic inferences about how the vulnerabilities of users change as they share new content in OSNs (or as their graph connectivity changes). We evaluate the generated models in real data. Our results show that the generated models determine with high accuracy whether a user profile is vulnerable to profile matching risk by only analyzing their publicly available information in the anonymous OSN. In addition, we develop optimization-based countermeasures to preserve the user's privacy as they share their OSN profile with third parties. We believe that this work will be crucial for OSN users to understand their privacy risks due to their public sharings and be more conscious about their online privacy.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"51 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":"127121303","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}
Polarization is an alarming trend in modern societies with serious implications on social cohesion and the democratic process. Typically, polarization manifests itself in the public discourse in politics, governance and ideology. In recent years, however, polarization arises increasingly in a wider range of issues, from identity and culture to healthcare and the environment. As the public and private discourse moves online, polarization feeds in and is fed by phenomena like fake news and hate speech. The identification and analysis of online polarization is challenging because of the massive scale, diversity, and unstructured nature of online content, and the rapid and unpredictable evolution of polarizing issues. Therefore, we need effective ways to identify, quantify, and represent polarization and polarizing topics algorithmically and at scale. In this work, we introduce POLAR - an unsupervised, large-scale framework for modeling and identifying polarizing topics in any domain, without prior domain-specific knowledge. POLAR comprises a processing pipeline that analyzes a corpus of an arbitrary number of news articles to construct a hierarchical knowledge graph that models polarization and identify polarizing topics discussed in the corpus. Our evaluation shows that POLAR is able to identify and rank polarizing topics accurately and efficiently.
{"title":"POLAR: a holistic framework for the modelling of polarization and identification of polarizing topics in news media","authors":"Demetris Paschalides, G. Pallis, M. Dikaiakos","doi":"10.1145/3487351.3489443","DOIUrl":"https://doi.org/10.1145/3487351.3489443","url":null,"abstract":"Polarization is an alarming trend in modern societies with serious implications on social cohesion and the democratic process. Typically, polarization manifests itself in the public discourse in politics, governance and ideology. In recent years, however, polarization arises increasingly in a wider range of issues, from identity and culture to healthcare and the environment. As the public and private discourse moves online, polarization feeds in and is fed by phenomena like fake news and hate speech. The identification and analysis of online polarization is challenging because of the massive scale, diversity, and unstructured nature of online content, and the rapid and unpredictable evolution of polarizing issues. Therefore, we need effective ways to identify, quantify, and represent polarization and polarizing topics algorithmically and at scale. In this work, we introduce POLAR - an unsupervised, large-scale framework for modeling and identifying polarizing topics in any domain, without prior domain-specific knowledge. POLAR comprises a processing pipeline that analyzes a corpus of an arbitrary number of news articles to construct a hierarchical knowledge graph that models polarization and identify polarizing topics discussed in the corpus. Our evaluation shows that POLAR is able to identify and rank polarizing topics accurately and efficiently.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"22 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":"122284752","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}
K. Miyazaki, T. Uchiba, F. Toriumi, Kenji Tanaka, Takeshi Sakaki
For efficient policy-making, a thorough recognition of controversial topics is crucial because the cost of unmitigated controversies would be extremely high for society. However, identifying controversial topics is costly. In this paper, we proposed a framework to search for controversial topics comprehensively. We then conducted a retrospective analysis of the controversial topics of COVID-19 with data obtained via Twitter in Japan as a case study of the framework. The results show that the proposed framework can effectively detect controversial topics that reflect current reality. Controversial topics tend to be about the government, medical matters, economy, and education; moreover, the controversy score had a low correlation with the traditional indicators-scale and sentiment of the topics-which suggests that the controversy score is a potentially important indicator to be obtained. We also discussed the difference between highly controversial topics and less controversial ones despite their large scale and sentiment.
{"title":"Retrospective analysis of controversial topics on COVID-19 in Japan","authors":"K. Miyazaki, T. Uchiba, F. Toriumi, Kenji Tanaka, Takeshi Sakaki","doi":"10.1145/3487351.3490963","DOIUrl":"https://doi.org/10.1145/3487351.3490963","url":null,"abstract":"For efficient policy-making, a thorough recognition of controversial topics is crucial because the cost of unmitigated controversies would be extremely high for society. However, identifying controversial topics is costly. In this paper, we proposed a framework to search for controversial topics comprehensively. We then conducted a retrospective analysis of the controversial topics of COVID-19 with data obtained via Twitter in Japan as a case study of the framework. The results show that the proposed framework can effectively detect controversial topics that reflect current reality. Controversial topics tend to be about the government, medical matters, economy, and education; moreover, the controversy score had a low correlation with the traditional indicators-scale and sentiment of the topics-which suggests that the controversy score is a potentially important indicator to be obtained. We also discussed the difference between highly controversial topics and less controversial ones despite their large scale and sentiment.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"427 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":"132907990","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}
Clustering is a challenging research task which could benefit a wide range of practical applications, including bioinformatics. It targets success by optimizing a number of objectives, a characteristic mostly ignored by clustering approaches. This paper describes a synthetic clustering algorithm which first applies multi-objective based approach to produce the alternative clustering solutions. Then the best clusters from each solution are selected and combined into a seed for a compact and effective solution which is expected to be better than all the individual solutions because it combines the best of each. This way, the developed algorithm may be classified as a fuzzy clustering approach because each object may belong to more than one cluster in the synthesized solution with a degree of membership in each cluster. Another interesting aspect of the algorithm is that it identifies the outliers. Further, a network is built from the relationships of the objects within the various clusters. The network is analyzed to reveal interesting discoveries not clearly reflected in the clustering outcome. The validity and applicability of the presented methodology has been assessed using synthetic and real data from the cancer.
{"title":"Combining multiple clustering and network analysis for discoveries in gene expression data","authors":"Sleiman Alhajj, A. Alhajj, S. Özyer","doi":"10.1145/3487351.3490961","DOIUrl":"https://doi.org/10.1145/3487351.3490961","url":null,"abstract":"Clustering is a challenging research task which could benefit a wide range of practical applications, including bioinformatics. It targets success by optimizing a number of objectives, a characteristic mostly ignored by clustering approaches. This paper describes a synthetic clustering algorithm which first applies multi-objective based approach to produce the alternative clustering solutions. Then the best clusters from each solution are selected and combined into a seed for a compact and effective solution which is expected to be better than all the individual solutions because it combines the best of each. This way, the developed algorithm may be classified as a fuzzy clustering approach because each object may belong to more than one cluster in the synthesized solution with a degree of membership in each cluster. Another interesting aspect of the algorithm is that it identifies the outliers. Further, a network is built from the relationships of the objects within the various clusters. The network is analyzed to reveal interesting discoveries not clearly reflected in the clustering outcome. The validity and applicability of the presented methodology has been assessed using synthetic and real data from the cancer.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"107 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":"132978955","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}
Social Resonance is a common socio-behavioral phenomenon in which users are more influenced by opinions that have similar vibes. That is, opinions from two different groups of users can mutually reinforce (or resonate with) each other to have an even stronger impact on the user. In this paper, we explore the powerful social resonance effect between social connections and other users in an eCommerce platform to improve recommendation. Specifically, we first formulate an item-aware user influence network that connects users who rate the same item. With the social network and item-aware user influence network, a novel graph-based mutual learning framework is proposed, which captures the resonance influence from both user local correlations and global connections. We then fuse these influence paths to predict the resonance-enhanced user preference towards items. Experiments on public benchmarks show the proposed approach outperforms state-of-the-art social recommendation methods.
{"title":"Vibe check: social resonance learning for enhanced recommendation","authors":"Yin Zhang, Yun He, James Caverlee","doi":"10.1145/3487351.3488335","DOIUrl":"https://doi.org/10.1145/3487351.3488335","url":null,"abstract":"Social Resonance is a common socio-behavioral phenomenon in which users are more influenced by opinions that have similar vibes. That is, opinions from two different groups of users can mutually reinforce (or resonate with) each other to have an even stronger impact on the user. In this paper, we explore the powerful social resonance effect between social connections and other users in an eCommerce platform to improve recommendation. Specifically, we first formulate an item-aware user influence network that connects users who rate the same item. With the social network and item-aware user influence network, a novel graph-based mutual learning framework is proposed, which captures the resonance influence from both user local correlations and global connections. We then fuse these influence paths to predict the resonance-enhanced user preference towards items. Experiments on public benchmarks show the proposed approach outperforms state-of-the-art social recommendation methods.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"7 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":"114954532","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}
Nowadays, social networking is popular. As such, numerous social networking sites (e.g., Facebook, YouTube, Instagram) are generating very large volumes of social data rapidly. Valuable knowledge and information is embedded into these big social data. As the social network can be very sparse, it is awaiting to be (a) compressed via social network data compression and (b) analyzed and mined via social network analysis and mining. We present in this paper a solution for compressing and mining social networks. It gives an interpretable compressed representation of sparse social network, and discovers interesting patterns from the social network. Results of our evaluation show the effectiveness of our solution in explaining the compression and mining of the sparse social network data.
{"title":"Compressing and mining social network data","authors":"Connor C. J. Hryhoruk, C. Leung","doi":"10.1145/3487351.3489472","DOIUrl":"https://doi.org/10.1145/3487351.3489472","url":null,"abstract":"Nowadays, social networking is popular. As such, numerous social networking sites (e.g., Facebook, YouTube, Instagram) are generating very large volumes of social data rapidly. Valuable knowledge and information is embedded into these big social data. As the social network can be very sparse, it is awaiting to be (a) compressed via social network data compression and (b) analyzed and mined via social network analysis and mining. We present in this paper a solution for compressing and mining social networks. It gives an interpretable compressed representation of sparse social network, and discovers interesting patterns from the social network. Results of our evaluation show the effectiveness of our solution in explaining the compression and mining of the sparse social network data.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"39 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":"116412497","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}
Social Capital is considered as the value that an actor draws from the network. It is measured as an ability to bond with others (bonding capital) as well as an ability to form a bridge that connects otherwise disconnected actors or groups in the network (bridging capital). In order to model the social capital, the strategic nature of forming links needs to be considered and baked within the network formation models. In this paper, we develop a strategic network formation game named Structural Hole Connections Game (shc) and define an associated allocation function that distributes the total network value to the actors in the network in such a way that captures their bonding and bridging social capital. Our proposed shc game generalizes the utility function that models both the bonding and bridging capabilities of an actor with high social capital. We first analytically deduct the efficient and stable networks of the shc game. Finally, we analyse a real-world social network of employees of an IT company and identify individuals with binding and bridging social profiles.
{"title":"Modelling social capital: the structural hole connections game","authors":"Faisal Ghaffar, Neil Hurley","doi":"10.1145/3487351.3488365","DOIUrl":"https://doi.org/10.1145/3487351.3488365","url":null,"abstract":"Social Capital is considered as the value that an actor draws from the network. It is measured as an ability to bond with others (bonding capital) as well as an ability to form a bridge that connects otherwise disconnected actors or groups in the network (bridging capital). In order to model the social capital, the strategic nature of forming links needs to be considered and baked within the network formation models. In this paper, we develop a strategic network formation game named Structural Hole Connections Game (shc) and define an associated allocation function that distributes the total network value to the actors in the network in such a way that captures their bonding and bridging social capital. Our proposed shc game generalizes the utility function that models both the bonding and bridging capabilities of an actor with high social capital. We first analytically deduct the efficient and stable networks of the shc game. Finally, we analyse a real-world social network of employees of an IT company and identify individuals with binding and bridging social profiles.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"28 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":"128807443","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}
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