Social sensing has emerged as a new sensing paradigm to observe the physical world by exploring the “wisdom of crowd” on social media. This paper focuses on the abnormal traffic event localization problem using social media sensing. Two critical challenges exist in the state-of-the-arts: i) “content-only inference”: the limited and unstructured content of a social media post provides little clue to accurately infer the locations of the reported traffic events; ii) “informal and scarce data”: the language of the social media post (e.g., tweet) is informal and the number of the posts that report the abnormal traffic events is often quite small. To address the above challenges, we develop SyntaxLoc, a syntax-based probabilistic learning framework to accurately identify the location entities by exploring the syntax of social media content. We perform extensive experiments to evaluate the SyntaxLoc framework through real world case studies in both New York City and Los Angeles. Evaluation results demonstrate significant performance gains of the SyntaxLoc framework over state-of-the-art baselines in terms of accurately identifying the location entities that can be directly used to locate the abnormal traffic events.
{"title":"A Syntax-based Learning Approach to Geo-locating Abnormal Traffic Events using Social Sensing","authors":"Yang Zhang, Xiangyu Dong, D. Zhang, Dong Wang","doi":"10.1145/3341161.3343708","DOIUrl":"https://doi.org/10.1145/3341161.3343708","url":null,"abstract":"Social sensing has emerged as a new sensing paradigm to observe the physical world by exploring the “wisdom of crowd” on social media. This paper focuses on the abnormal traffic event localization problem using social media sensing. Two critical challenges exist in the state-of-the-arts: i) “content-only inference”: the limited and unstructured content of a social media post provides little clue to accurately infer the locations of the reported traffic events; ii) “informal and scarce data”: the language of the social media post (e.g., tweet) is informal and the number of the posts that report the abnormal traffic events is often quite small. To address the above challenges, we develop SyntaxLoc, a syntax-based probabilistic learning framework to accurately identify the location entities by exploring the syntax of social media content. We perform extensive experiments to evaluate the SyntaxLoc framework through real world case studies in both New York City and Los Angeles. Evaluation results demonstrate significant performance gains of the SyntaxLoc framework over state-of-the-art baselines in terms of accurately identifying the location entities that can be directly used to locate the abnormal traffic events.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"119 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":"125204306","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}
Fernando H. Calderon, Li-Kai Cheng, Ming-Jen Lin, Yen-Hao Huang, Yi-Shin Chen
“Echo chamber” is a metaphorical description of a situation in which beliefs are amplified inside a closed network, and social media platforms provide an environment that is well-suited to this phenomenon. Depending on the scale of the echo chamber, a user's judgment of different opinions may be restricted. The current study focuses on detecting echoing interaction between a post and its related comments to then quantify the predominating degree of echo chamber behavior on Facebook pages. To enable such detection, two content-based features are designed; the first aids stance representation of comments on a particular discussion topic, and the second focuses on the type and intensity of emotion elicited by a subject. This work also introduces data-driven semi-supervised approaches to extract such features from social media data.
{"title":"Content-Based Echo Chamber Detection on Social Media Platforms","authors":"Fernando H. Calderon, Li-Kai Cheng, Ming-Jen Lin, Yen-Hao Huang, Yi-Shin Chen","doi":"10.1145/3341161.3343689","DOIUrl":"https://doi.org/10.1145/3341161.3343689","url":null,"abstract":"“Echo chamber” is a metaphorical description of a situation in which beliefs are amplified inside a closed network, and social media platforms provide an environment that is well-suited to this phenomenon. Depending on the scale of the echo chamber, a user's judgment of different opinions may be restricted. The current study focuses on detecting echoing interaction between a post and its related comments to then quantify the predominating degree of echo chamber behavior on Facebook pages. To enable such detection, two content-based features are designed; the first aids stance representation of comments on a particular discussion topic, and the second focuses on the type and intensity of emotion elicited by a subject. This work also introduces data-driven semi-supervised approaches to extract such features from social media data.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"32 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":"127797128","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}
Patryk Pazura, Jarosław Jankowski, Kamil Bortko, Piotr Bartków
In recent years, bustling online communities have focused a lot of attention on research dealing with information spreading. Through acquired knowledge about the characteristics of information spreading processes, we are able to influence their dynamics via the enhancement of propagation properties or by changing them to decrease their spread within a network. One of approaches is adding or removing connections within a network. While optimal linking within complex networks requires extensive computational resources, in this investigation, we focus on the optimization of the topology of small graphs within larger network structures. The study shows how the enhancement of propagation properties within small networks is preserved in bigger networks based on connected smaller graphs. We compare the results from combined small graphs with added links providing optimal spread and networks with additional random linking. The results show that improvements in linking within small sub-graphs with optimal linking improves the diffusional properties of the whole network.
{"title":"Increasing the Diffusional Characteristics of Networks Through Optimal Topology Changes within Sub-graphs","authors":"Patryk Pazura, Jarosław Jankowski, Kamil Bortko, Piotr Bartków","doi":"10.1145/3341161.3344823","DOIUrl":"https://doi.org/10.1145/3341161.3344823","url":null,"abstract":"In recent years, bustling online communities have focused a lot of attention on research dealing with information spreading. Through acquired knowledge about the characteristics of information spreading processes, we are able to influence their dynamics via the enhancement of propagation properties or by changing them to decrease their spread within a network. One of approaches is adding or removing connections within a network. While optimal linking within complex networks requires extensive computational resources, in this investigation, we focus on the optimization of the topology of small graphs within larger network structures. The study shows how the enhancement of propagation properties within small networks is preserved in bigger networks based on connected smaller graphs. We compare the results from combined small graphs with added links providing optimal spread and networks with additional random linking. The results show that improvements in linking within small sub-graphs with optimal linking improves the diffusional properties of the whole network.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"14 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":"134145873","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}
Climbing the career ladder to a senior executive position is a long and complex process that, nevertheless, many people are trying to master. Over the last decades, the number of people providing their CVs on professional online social networks, such as LinkedIn is growing. New methods of pattern detection raise the question of whether online CVs provide insights into career patterns and paths. The respective hypothesis is that online CVs map people“s careers and therefore build the ideal data set to detect career patterns. To test this hypothesis, 100.006 online CVs were downloaded and preprocessed. This paper presents initial results of one educational and one internship variable. Whereas a higher degree positively predicts career level, having made an internship negatively relates to career level. These results reveal that rather than objectively mirroring people“s career trajectories, online career platforms provide selective information. The information of online CVs and the respective career level is intermingled, i.e. people with a high career level present different parts of their careers than people on lower levels. Furthermore, self-presentational effects might have an impact. The effect on similar research and possible implications are discussed.
{"title":"The curse of self-presentation: Looking for career patterns in online CVs","authors":"Johanna M. Werz, Valerie Varney, I. Isenhardt","doi":"10.1145/3341161.3343681","DOIUrl":"https://doi.org/10.1145/3341161.3343681","url":null,"abstract":"Climbing the career ladder to a senior executive position is a long and complex process that, nevertheless, many people are trying to master. Over the last decades, the number of people providing their CVs on professional online social networks, such as LinkedIn is growing. New methods of pattern detection raise the question of whether online CVs provide insights into career patterns and paths. The respective hypothesis is that online CVs map people“s careers and therefore build the ideal data set to detect career patterns. To test this hypothesis, 100.006 online CVs were downloaded and preprocessed. This paper presents initial results of one educational and one internship variable. Whereas a higher degree positively predicts career level, having made an internship negatively relates to career level. These results reveal that rather than objectively mirroring people“s career trajectories, online career platforms provide selective information. The information of online CVs and the respective career level is intermingled, i.e. people with a high career level present different parts of their careers than people on lower levels. Furthermore, self-presentational effects might have an impact. The effect on similar research and possible implications are discussed.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"148 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":"134190356","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}
Millions of health-related messages and fresh communications can reveal important public health issues. New Drugs, Diseases, Adverse Drug Reactions (ADRs) keep appearing on social media in new Unicode versions. In particular, generative Model for both Sentiment analysis (SA) and Naturel Language Understanding (NLU) requires medical human labeled data or making use of resources for weak supervision that operates with the ignorance and the inability to define related-medication targets, and results in inaccurate sentiment prediction performance. The frequent use of informal medical language, nonstandard format and abbreviation forms, as well as typos in social media messages has to be taken into account. We probe the transition-based approach between patients language used in social media messages and formal medical language used in the descriptions of medical concepts in a standard ontology[21] to be formal input of our neural network model. At this end, we propose daily life patients Sentiment Analysis model based on hybrid embedding vocabulary for related-medication text under distributed dependency, and concepts translation methodology by incorporating medical knowledge from social media and real life medical science systems. The proposed neural network layers is shared between medical concept Normalization model and sentiment prediction model in order to understand and leverage related-sentiment information behind conceptualized features in Multiple context. The experiments were performed on various real world scenarios where limited resources in this case.
{"title":"Daily life patients Sentiment Analysis model based on well-encoded embedding vocabulary for related-medication text","authors":"Hanane Grissette, E. Nfaoui","doi":"10.1145/3341161.3343854","DOIUrl":"https://doi.org/10.1145/3341161.3343854","url":null,"abstract":"Millions of health-related messages and fresh communications can reveal important public health issues. New Drugs, Diseases, Adverse Drug Reactions (ADRs) keep appearing on social media in new Unicode versions. In particular, generative Model for both Sentiment analysis (SA) and Naturel Language Understanding (NLU) requires medical human labeled data or making use of resources for weak supervision that operates with the ignorance and the inability to define related-medication targets, and results in inaccurate sentiment prediction performance. The frequent use of informal medical language, nonstandard format and abbreviation forms, as well as typos in social media messages has to be taken into account. We probe the transition-based approach between patients language used in social media messages and formal medical language used in the descriptions of medical concepts in a standard ontology[21] to be formal input of our neural network model. At this end, we propose daily life patients Sentiment Analysis model based on hybrid embedding vocabulary for related-medication text under distributed dependency, and concepts translation methodology by incorporating medical knowledge from social media and real life medical science systems. The proposed neural network layers is shared between medical concept Normalization model and sentiment prediction model in order to understand and leverage related-sentiment information behind conceptualized features in Multiple context. The experiments were performed on various real world scenarios where limited resources in this case.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"88 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":"134253962","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}
Data-driven analysis of large social networks has attracted a great deal of research interest. In this paper, we investigate 120 real social networks and their measurement-calibrated synthetic counterparts generated by four well-known network models. We investigate the structural properties of the networks revealing the correlation profiles of graph metrics across various social domains (friendship networks, communication networks, and collaboration networks). We find that the correlation patterns differ across domains. We identify a nonredundant set of metrics to describe social networks. We study which topological characteristics of real networks the models can or cannot capture. We find that the goodness-of-fit of the network models depends on the domains. Furthermore, while 2K and stochastic block models lack the capability of generating graphs with large diameter and high clustering coefficient at the same time, they can still be used to mimic social networks relatively efficiently.
{"title":"On the Structural Properties of Social Networks and their Measurement-calibrated Synthetic Counterparts","authors":"Marcell Nagy, Roland Molontay","doi":"10.1145/3341161.3343686","DOIUrl":"https://doi.org/10.1145/3341161.3343686","url":null,"abstract":"Data-driven analysis of large social networks has attracted a great deal of research interest. In this paper, we investigate 120 real social networks and their measurement-calibrated synthetic counterparts generated by four well-known network models. We investigate the structural properties of the networks revealing the correlation profiles of graph metrics across various social domains (friendship networks, communication networks, and collaboration networks). We find that the correlation patterns differ across domains. We identify a nonredundant set of metrics to describe social networks. We study which topological characteristics of real networks the models can or cannot capture. We find that the goodness-of-fit of the network models depends on the domains. Furthermore, while 2K and stochastic block models lack the capability of generating graphs with large diameter and high clustering coefficient at the same time, they can still be used to mimic social networks relatively efficiently.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"76 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":"115329154","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}
We propose a novel formalization of roles in social networks that unifies the most commonly used definitions of role equivalence. As one consequence, we obtain a single, straightforward proof that role equivalences form lattices. Our formalization focuses on the evolution of roles from arbitrary initial conditions and thereby generalizes notions of relative and iterated roles that have been suggested previously. In addition to the unified structure result this provides a micro-foundation for the emergence of roles. Considering the genesis of roles may explain, and help overcome, the problem that social networks rarely exhibit interesting role equivalences of the traditional kind. Finally, we hint at ways to further generalize the role concept to multivariate networks.
{"title":"The Evolution of Roles","authors":"Julian Müller, U. Brandes","doi":"10.1145/3341161.3342889","DOIUrl":"https://doi.org/10.1145/3341161.3342889","url":null,"abstract":"We propose a novel formalization of roles in social networks that unifies the most commonly used definitions of role equivalence. As one consequence, we obtain a single, straightforward proof that role equivalences form lattices. Our formalization focuses on the evolution of roles from arbitrary initial conditions and thereby generalizes notions of relative and iterated roles that have been suggested previously. In addition to the unified structure result this provides a micro-foundation for the emergence of roles. Considering the genesis of roles may explain, and help overcome, the problem that social networks rarely exhibit interesting role equivalences of the traditional kind. Finally, we hint at ways to further generalize the role concept to multivariate networks.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"27 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":"117066133","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}
Dionisios N. Sotiropoulos, Ifigeneia Georgoula, Christos Bilanakos
This paper presents a non-linear optimization methodology for determining the Nash Equilibrium (NE) solutions of a non-cooperative two-player game. Each player, in particular, is trying to maximize a rational profit function within a continuous action space. The game arises in the context of a duopolistic network environment where two identical rival firms are competing to maximize their influence over a single consumer. Specifically, we consider a weighted and strongly connected network which mediates the opinion formation processes concerning the perceived qualities of their products. Obtaining the NE solutions for such a game is an extremely difficult task which cannot be analytically addressed, even if additional simplifying assumptions are imposed on the exogenous parameters of the model. Our approach, obtains the required NE solutions by combining the Karush-Kuhn-Tucker (KKT) conditions associated with the original optimization tasks into a single-objective nonlinear maximization problem under nonlinear constrains. The resulting optimization problem is, ultimately, solved through the utilization of the Sequential Quadratic Programming (SQP) algorithm which constitutes a state-of-the-art method for nonlinear optimization problems. The validity of our work is justified through the conduction of a series of experiments in which we simulated the best response-based dynamical behaviour of the two agents in the network that make strategic decisions. Juxtaposing the intersection points of the acquired best response curves against the NE solutions obtained by the proposed nonlinear optimization methodology verifies that the corresponding solution points coincide.
{"title":"Optimal Influence Strategies in an Oligopolistic Competition Network Environment","authors":"Dionisios N. Sotiropoulos, Ifigeneia Georgoula, Christos Bilanakos","doi":"10.1145/3341161.3343691","DOIUrl":"https://doi.org/10.1145/3341161.3343691","url":null,"abstract":"This paper presents a non-linear optimization methodology for determining the Nash Equilibrium (NE) solutions of a non-cooperative two-player game. Each player, in particular, is trying to maximize a rational profit function within a continuous action space. The game arises in the context of a duopolistic network environment where two identical rival firms are competing to maximize their influence over a single consumer. Specifically, we consider a weighted and strongly connected network which mediates the opinion formation processes concerning the perceived qualities of their products. Obtaining the NE solutions for such a game is an extremely difficult task which cannot be analytically addressed, even if additional simplifying assumptions are imposed on the exogenous parameters of the model. Our approach, obtains the required NE solutions by combining the Karush-Kuhn-Tucker (KKT) conditions associated with the original optimization tasks into a single-objective nonlinear maximization problem under nonlinear constrains. The resulting optimization problem is, ultimately, solved through the utilization of the Sequential Quadratic Programming (SQP) algorithm which constitutes a state-of-the-art method for nonlinear optimization problems. The validity of our work is justified through the conduction of a series of experiments in which we simulated the best response-based dynamical behaviour of the two agents in the network that make strategic decisions. Juxtaposing the intersection points of the acquired best response curves against the NE solutions obtained by the proposed nonlinear optimization methodology verifies that the corresponding solution points coincide.","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":"124743257","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}
Influence maximization in social networks has been intensively studied in recent years, where the goal is to find a small set of seed nodes in a social network that maximizes the spread of influence according to a diffusion model. Recent research on influence maximization mainly focuses on incorporating either user opinions or competitive settings in the influence diffusion model. In many real-world applications, however, the influence diffusion process often involves both real-valued opinions from users and multiple parties that are competing with each other. In this paper, we study the problem of competitive opinion maximization, where the game of influence diffusion includes multiple competing products and the goal is to maximize the total opinions of activated users by each product. This problem is very challenging because it is #P-hard and no longer keeps the property of submodularity. We propose a novel model, called ICOM (Iterative Competitive Opinion Maximization), that can effectively and efficiently maximize the total opinions in competitive games by taking user opinions as well as the competitor's strategy into account. Different from existing influence maximization methods, we inhibit the spread of negative opinions and search for the optimal response to opponents' choices of seed nodes. We apply iterative inference based on a greedy algorithm to reduce the computational complexity. Empirical studies on real-world datasets demonstrate that comparing with several baseline methods, our approach can effectively and efficiently improve the total opinions achieved by the promoted product in the competitive network.
{"title":"Competitive Opinion Maximization in Social Networks","authors":"Jianjun Luo, Xinyue Liu, Xiangnan Kong","doi":"10.1145/3341161.3342899","DOIUrl":"https://doi.org/10.1145/3341161.3342899","url":null,"abstract":"Influence maximization in social networks has been intensively studied in recent years, where the goal is to find a small set of seed nodes in a social network that maximizes the spread of influence according to a diffusion model. Recent research on influence maximization mainly focuses on incorporating either user opinions or competitive settings in the influence diffusion model. In many real-world applications, however, the influence diffusion process often involves both real-valued opinions from users and multiple parties that are competing with each other. In this paper, we study the problem of competitive opinion maximization, where the game of influence diffusion includes multiple competing products and the goal is to maximize the total opinions of activated users by each product. This problem is very challenging because it is #P-hard and no longer keeps the property of submodularity. We propose a novel model, called ICOM (Iterative Competitive Opinion Maximization), that can effectively and efficiently maximize the total opinions in competitive games by taking user opinions as well as the competitor's strategy into account. Different from existing influence maximization methods, we inhibit the spread of negative opinions and search for the optimal response to opponents' choices of seed nodes. We apply iterative inference based on a greedy algorithm to reduce the computational complexity. Empirical studies on real-world datasets demonstrate that comparing with several baseline methods, our approach can effectively and efficiently improve the total opinions achieved by the promoted product in the competitive network.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 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":"124848899","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}
Role discovery and community detection in networks are two essential tasks in network analytics where the role denotes the global structural patterns of nodes in networks and the community represents the local connections of nodes in networks. Previous studies viewed these two tasks orthogonally and solved them independently while the relation between them has been totally neglected. However, it is intuitive that roles and communities in a network are correlated and complementary to each other. In this paper, we propose a novel model for simultaneous roles and communities detection (REACT) in networks. REACT uses non-negative matrix tri-factorization (NMTF) to detect roles and communities and utilizes L2,1 norm as the regularization to capture the diversity relation between roles and communities. The proposed model has several advantages comparing with other existing methods: (1) it incorporates the diversity relation between roles and communities to detect them simultaneously using a unified model, and (2) it provides extra information about the interaction patterns between roles and between communities using NMTF. To analyze the performance of REACT, we conduct experiments on several real-world SNs from different domains. By comparing with state-of-the-art community detection and role discovery methods, the obtained results demonstrate REACT performs best for both role and community detection tasks. Moreover, our model provides a better interpretation for the interaction patterns between communities and between roles.
{"title":"Joint Role and Community Detection in Networks via L2,1 Norm Regularized Nonnegative Matrix Tri-Factorization","authors":"Yulong Pei, G. Fletcher, Mykola Pechenizkiy","doi":"10.1145/3341161.3342886","DOIUrl":"https://doi.org/10.1145/3341161.3342886","url":null,"abstract":"Role discovery and community detection in networks are two essential tasks in network analytics where the role denotes the global structural patterns of nodes in networks and the community represents the local connections of nodes in networks. Previous studies viewed these two tasks orthogonally and solved them independently while the relation between them has been totally neglected. However, it is intuitive that roles and communities in a network are correlated and complementary to each other. In this paper, we propose a novel model for simultaneous roles and communities detection (REACT) in networks. REACT uses non-negative matrix tri-factorization (NMTF) to detect roles and communities and utilizes L2,1 norm as the regularization to capture the diversity relation between roles and communities. The proposed model has several advantages comparing with other existing methods: (1) it incorporates the diversity relation between roles and communities to detect them simultaneously using a unified model, and (2) it provides extra information about the interaction patterns between roles and between communities using NMTF. To analyze the performance of REACT, we conduct experiments on several real-world SNs from different domains. By comparing with state-of-the-art community detection and role discovery methods, the obtained results demonstrate REACT performs best for both role and community detection tasks. Moreover, our model provides a better interpretation for the interaction patterns between communities and between roles.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"57 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":"121766532","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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