Saif Ahmed, Md. Tanvir Alam Anik, Mashrura Tasnim, H. Ferdous
Using Social Network Sites (SNS) as an information source has drawn the attention of the researchers for a while now. There has been many works that analyzed the types and topics of questions people ask in these networks and why. Topics like what motivate people to answer such queries, how to integrate the traditional search engines and SNS together are also well investigated. In this paper, we focus on a relevant but different issue - how SNS search varies in developed and developing regions of the world and why. Analyzing 470 status messages collected from a widely used SNS, we have observed that, unavailability and inadequacy of information on web in developing countries play a significant role to motivate users using SNS for information retrieval.With established statistics of Internet usage, e-Governance, and our experimental data analysis, we have tried to emphasize the differences between social search and traditional web-search and provided insight that one might require to consider while developing any application for SNS based searching.
{"title":"Statistical analysis and implications of SNS search in under-developed countries","authors":"Saif Ahmed, Md. Tanvir Alam Anik, Mashrura Tasnim, H. Ferdous","doi":"10.1145/2541016.2541041","DOIUrl":"https://doi.org/10.1145/2541016.2541041","url":null,"abstract":"Using Social Network Sites (SNS) as an information source has drawn the attention of the researchers for a while now. There has been many works that analyzed the types and topics of questions people ask in these networks and why. Topics like what motivate people to answer such queries, how to integrate the traditional search engines and SNS together are also well investigated. In this paper, we focus on a relevant but different issue - how SNS search varies in developed and developing regions of the world and why. Analyzing 470 status messages collected from a widely used SNS, we have observed that, unavailability and inadequacy of information on web in developing countries play a significant role to motivate users using SNS for information retrieval.With established statistics of Internet usage, e-Governance, and our experimental data analysis, we have tried to emphasize the differences between social search and traditional web-search and provided insight that one might require to consider while developing any application for SNS based searching.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134545196","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}
Mohammad Rashedul Hasan, Mohamed Shehab, Ali Noorollahiravari
Diaspora is a decentralized online social networking platform where user profiles are hosted in multiple Diaspora nodes (pods) and the social connections can exist across different pods. User profile migration is a promising feature that would enable users to seamlessly migrate their profile data between different pods. However, to the best of our knowledge, there has been no research done on how this data portability may affect the user distribution and the performance of the pods. In this paper, our goal is to design an approach that facilitates the users to choose appropriate pods that would ensure better service quality. We propose a decentralized game-theoretic approach that is based on user's local neighborhood information and the quality of the pods. We have analytically determined, and experimentally substantiated, that through the proposed profile migration approach the users of Diaspora reach a stable and balanced distribution that improves their overall experience in respective pods.
{"title":"Game-theoretic approach for user migration in Diaspora","authors":"Mohammad Rashedul Hasan, Mohamed Shehab, Ali Noorollahiravari","doi":"10.1145/2492517.2492648","DOIUrl":"https://doi.org/10.1145/2492517.2492648","url":null,"abstract":"Diaspora is a decentralized online social networking platform where user profiles are hosted in multiple Diaspora nodes (pods) and the social connections can exist across different pods. User profile migration is a promising feature that would enable users to seamlessly migrate their profile data between different pods. However, to the best of our knowledge, there has been no research done on how this data portability may affect the user distribution and the performance of the pods. In this paper, our goal is to design an approach that facilitates the users to choose appropriate pods that would ensure better service quality. We propose a decentralized game-theoretic approach that is based on user's local neighborhood information and the quality of the pods. We have analytically determined, and experimentally substantiated, that through the proposed profile migration approach the users of Diaspora reach a stable and balanced distribution that improves their overall experience in respective pods.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123098190","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}
Sybil attacks in social and information systems have serious security implications. Out of many defence schemes, Graph-based Sybil Detection (GSD) had the greatest attention by both academia and industry. Even though many GSD algorithms exist, there is no analytical framework to reason about their design, especially as they make different assumptions about the used adversary and graph models. In this paper, we bridge this knowledge gap and present a unified framework for systematic evaluation of GSD algorithms. We used this framework to show that GSD algorithms should be designed to find local community structures around known non-Sybil identities, while incrementally tracking changes in the graph as it evolves over time.
{"title":"Graph-based Sybil Detection in social and information systems","authors":"Yazan Boshmaf, K. Beznosov, M. Ripeanu","doi":"10.1145/2492517.2492568","DOIUrl":"https://doi.org/10.1145/2492517.2492568","url":null,"abstract":"Sybil attacks in social and information systems have serious security implications. Out of many defence schemes, Graph-based Sybil Detection (GSD) had the greatest attention by both academia and industry. Even though many GSD algorithms exist, there is no analytical framework to reason about their design, especially as they make different assumptions about the used adversary and graph models. In this paper, we bridge this knowledge gap and present a unified framework for systematic evaluation of GSD algorithms. We used this framework to show that GSD algorithms should be designed to find local community structures around known non-Sybil identities, while incrementally tracking changes in the graph as it evolves over time.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116860641","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}
The proliferation of social networks in digital media has proved to be fruitful, but this rise in popularity is accompanied by user privacy concerns. Social network data has been published in various ways and preserving the privacy of individuals in the published data has become an important concern. Several algorithms have been developed for privacy preservation in relational data, but these algorithms cannot be applied directly to social networks as the nodes here have structural properties along with labels. In this paper, we propose an algorithm to achieve k-anonymity and l-diversity in social network data which provides structural anonymity along with sensitive attribute protection. The proposed algorithm uses novel edge addition techniques which are also presented in this paper. We also propose a concept of partial anonymity to reduce anonymization cost for d>1. The empirical study shows that our algorithm requires significantly less number of edge additions for anonymization of social network data and has a substantially lower running time than the other algorithms previously proposed in the field.
{"title":"GASNA: Greedy algorithm for social network anonymization","authors":"Mayank Singh, Shishodia Sumeet, Jain B K Tripathy","doi":"10.1145/2492517.2500267","DOIUrl":"https://doi.org/10.1145/2492517.2500267","url":null,"abstract":"The proliferation of social networks in digital media has proved to be fruitful, but this rise in popularity is accompanied by user privacy concerns. Social network data has been published in various ways and preserving the privacy of individuals in the published data has become an important concern. Several algorithms have been developed for privacy preservation in relational data, but these algorithms cannot be applied directly to social networks as the nodes here have structural properties along with labels. In this paper, we propose an algorithm to achieve k-anonymity and l-diversity in social network data which provides structural anonymity along with sensitive attribute protection. The proposed algorithm uses novel edge addition techniques which are also presented in this paper. We also propose a concept of partial anonymity to reduce anonymization cost for d>1. The empirical study shows that our algorithm requires significantly less number of edge additions for anonymization of social network data and has a substantially lower running time than the other algorithms previously proposed in the field.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124969431","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}
E. Briscoe, D. S. Appling, IV RudolphLouisMappus, Heather Hayes
The increasing proliferation of social media results in users that are forced to ascertain the truthfulness of information that they encounter from unknown sources using a variety of indicators (e.g. explicit ratings, profile information, etc.). Through human-subject experimentation with an online social network-style platform, our study focuses on the determination of credibility in ego-centric networks based on subjects observing social network properties such as degree centrality and geodesic distance. Using manipulated social network graphs, we find that corroboration and degree centrality are most utilized by subjects as indicators of credibility. We discuss the implications of the use of social network graph structural properties and use principal components analysis to visualize the reduced dimensional space.
{"title":"Determining credibility from social network structure","authors":"E. Briscoe, D. S. Appling, IV RudolphLouisMappus, Heather Hayes","doi":"10.1145/2492517.2492574","DOIUrl":"https://doi.org/10.1145/2492517.2492574","url":null,"abstract":"The increasing proliferation of social media results in users that are forced to ascertain the truthfulness of information that they encounter from unknown sources using a variety of indicators (e.g. explicit ratings, profile information, etc.). Through human-subject experimentation with an online social network-style platform, our study focuses on the determination of credibility in ego-centric networks based on subjects observing social network properties such as degree centrality and geodesic distance. Using manipulated social network graphs, we find that corroboration and degree centrality are most utilized by subjects as indicators of credibility. We discuss the implications of the use of social network graph structural properties and use principal components analysis to visualize the reduced dimensional space.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125907409","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}
Manish Gupta, Jing Gao, Xifeng Yan, H. Çam, Jiawei Han
In the real world, various systems can be modeled using heterogeneous networks which consist of entities of different types. People like to discover groups (or cliques) of entities linked to each other with rare and surprising associations from such networks. We define such anomalous cliques as Association-Based Clique Outliers (ABCOutliers) for heterogeneous information networks, and design effective approaches to detect them. The need to find such outlier cliques from networks can be formulated as a conjunctive select query consisting of a set of (type, predicate) pairs. Answering such conjunctive queries efficiently involves two main challenges: (1) computing all matching cliques which satisfy the query and (2) ranking such results based on the rarity and the interestingness of the associations among entities in the cliques. In this paper, we address these two challenges as follows. First, we introduce a new low-cost graph index to assist clique matching. Second, we define the outlierness of an association between two entities based on their attribute values and provide a methodology to efficiently compute such outliers given a conjunctive select query. Experimental results on several synthetic datasets and the Wikipedia dataset containing thousands of entities show the effectiveness of the proposed approach in computing interesting ABCOutliers.
{"title":"On detecting Association-Based Clique Outliers in heterogeneous information networks","authors":"Manish Gupta, Jing Gao, Xifeng Yan, H. Çam, Jiawei Han","doi":"10.1145/2492517.2492526","DOIUrl":"https://doi.org/10.1145/2492517.2492526","url":null,"abstract":"In the real world, various systems can be modeled using heterogeneous networks which consist of entities of different types. People like to discover groups (or cliques) of entities linked to each other with rare and surprising associations from such networks. We define such anomalous cliques as Association-Based Clique Outliers (ABCOutliers) for heterogeneous information networks, and design effective approaches to detect them. The need to find such outlier cliques from networks can be formulated as a conjunctive select query consisting of a set of (type, predicate) pairs. Answering such conjunctive queries efficiently involves two main challenges: (1) computing all matching cliques which satisfy the query and (2) ranking such results based on the rarity and the interestingness of the associations among entities in the cliques. In this paper, we address these two challenges as follows. First, we introduce a new low-cost graph index to assist clique matching. Second, we define the outlierness of an association between two entities based on their attribute values and provide a methodology to efficiently compute such outliers given a conjunctive select query. Experimental results on several synthetic datasets and the Wikipedia dataset containing thousands of entities show the effectiveness of the proposed approach in computing interesting ABCOutliers.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123273196","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}
Automation of data collection using online resources has led to significant changes in traditional practices of social network analysis. Social network analysis has been an active research field for many decades; however, most of the early work employed very small datasets. In this paper, a number of issues with traditional practices of social network analysis in the context of dynamic, large-scale social networks are pointed out. Given the continuously evolving nature of modern online social networking, we postulate that social network analysis solutions based on incremental algorithms will become more important to address high computation times for large, streaming, over-time datasets. Incremental algorithms can benefit from early pruning by updating the affected parts only when an incremental update is made in the network. This paper provides an example of this case by demonstrating the design of an incremental closeness centrality algorithm that supports efficient computation of all-pairs of shortest paths and closeness centrality in dynamic social networks that are continuously updated by addition, removal, and modification of nodes and edges. Our results obtained on various synthetic and real-life datasets provide significant speedups over the most commonly used method of computing closeness centrality, suggesting that incremental algorithm design is a fruitful research area for social network analysts.
{"title":"Incremental closeness centrality for dynamically changing social networks","authors":"Miray Kas, Kathleen M. Carley, L. Carley","doi":"10.1145/2492517.2500270","DOIUrl":"https://doi.org/10.1145/2492517.2500270","url":null,"abstract":"Automation of data collection using online resources has led to significant changes in traditional practices of social network analysis. Social network analysis has been an active research field for many decades; however, most of the early work employed very small datasets. In this paper, a number of issues with traditional practices of social network analysis in the context of dynamic, large-scale social networks are pointed out. Given the continuously evolving nature of modern online social networking, we postulate that social network analysis solutions based on incremental algorithms will become more important to address high computation times for large, streaming, over-time datasets. Incremental algorithms can benefit from early pruning by updating the affected parts only when an incremental update is made in the network. This paper provides an example of this case by demonstrating the design of an incremental closeness centrality algorithm that supports efficient computation of all-pairs of shortest paths and closeness centrality in dynamic social networks that are continuously updated by addition, removal, and modification of nodes and edges. Our results obtained on various synthetic and real-life datasets provide significant speedups over the most commonly used method of computing closeness centrality, suggesting that incremental algorithm design is a fruitful research area for social network analysts.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127009094","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}
Jayant Venkatanathan, E. Karapanos, V. Kostakos, Jorge Gonçalves
In this paper we adopt a network science approach to investigate empathy and its implications for online social networks. We demonstrate that empathy is closely linked to social capital - the findings suggest that individuals higher on cognitive empathic skill are overall likely to report both higher bridging and higher bonding social capital. On the other hand, attributes of network structure around the individual, quantified through networks analysis metrics, were related to cognitive empathy. Further, an examination of the interplay between network structure, social capital and empathy suggests that empathy facilitates the relation between network structure and social capital previously reported in literature. We discuss the implications of our findings for the understanding of empathy in the context of online social networks and for the design of these systems.
{"title":"A network science approach to Modelling and predicting empathy","authors":"Jayant Venkatanathan, E. Karapanos, V. Kostakos, Jorge Gonçalves","doi":"10.1145/2492517.2500295","DOIUrl":"https://doi.org/10.1145/2492517.2500295","url":null,"abstract":"In this paper we adopt a network science approach to investigate empathy and its implications for online social networks. We demonstrate that empathy is closely linked to social capital - the findings suggest that individuals higher on cognitive empathic skill are overall likely to report both higher bridging and higher bonding social capital. On the other hand, attributes of network structure around the individual, quantified through networks analysis metrics, were related to cognitive empathy. Further, an examination of the interplay between network structure, social capital and empathy suggests that empathy facilitates the relation between network structure and social capital previously reported in literature. We discuss the implications of our findings for the understanding of empathy in the context of online social networks and for the design of these systems.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"406 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114936024","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}
Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this paper, we study the problem of link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new link prediction framework, Link Prediction using Social Features (LPSF), which weights the network using a similarity function based on features extracted from patterns of prominent interactions across the network.
{"title":"Link prediction in multi-relational collaboration networks","authors":"Xi Wang, G. Sukthankar","doi":"10.1145/2492517.2492584","DOIUrl":"https://doi.org/10.1145/2492517.2492584","url":null,"abstract":"Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this paper, we study the problem of link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new link prediction framework, Link Prediction using Social Features (LPSF), which weights the network using a similarity function based on features extracted from patterns of prominent interactions across the network.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122623124","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}
Network structure and content in microblogging sites like Twitter influence each other - user A on Twitter follows user B for the tweets that B posts on the network, and A may then re-tweet the content shared by B to his/her own followers. In this paper, we propose a probabilistic model to jointly model link communities and content topics by leveraging both the social graph and the content shared by users. We model a community as a distribution over users, use it as a source for topics of interest, and jointly infer both communities and topics using Gibbs sampling. While modeling communities using the social graph, or modeling topics using content have received a great deal of attention, a few recent approaches try to model topics in content-sharing platforms using both content and social graph. Our work differs from the existing generative models in that we explicitly model the social graph of users along with the user-generated content, mimicking how the two entities co-evolve in content-sharing platforms. Recent studies have found Twitter to be more of a content-sharing network and less a social network, and it seems hard to detect tightly knit communities from the follower-followee links. Still, the question of whether we can extract Twitter communities using both links and content is open. In this paper, we answer this question in the affirmative. Our model discovers coherent communities and topics, as evinced by qualitative results on sub-graphs of Twitter users. Furthermore, we evaluate our model on the task of predicting follower-followee links. We show that joint modeling of links and content significantly improves link prediction performance on a sub-graph of Twitter (consisting of about 0.7 million users and over 27 million tweets), compared to generative models based on only structure or only content and paths-based methods such as Katz.
{"title":"Community detection in content-sharing social networks","authors":"Nagarajan Natarajan, P. Sen, V. Chaoji","doi":"10.1145/2492517.2492546","DOIUrl":"https://doi.org/10.1145/2492517.2492546","url":null,"abstract":"Network structure and content in microblogging sites like Twitter influence each other - user A on Twitter follows user B for the tweets that B posts on the network, and A may then re-tweet the content shared by B to his/her own followers. In this paper, we propose a probabilistic model to jointly model link communities and content topics by leveraging both the social graph and the content shared by users. We model a community as a distribution over users, use it as a source for topics of interest, and jointly infer both communities and topics using Gibbs sampling. While modeling communities using the social graph, or modeling topics using content have received a great deal of attention, a few recent approaches try to model topics in content-sharing platforms using both content and social graph. Our work differs from the existing generative models in that we explicitly model the social graph of users along with the user-generated content, mimicking how the two entities co-evolve in content-sharing platforms. Recent studies have found Twitter to be more of a content-sharing network and less a social network, and it seems hard to detect tightly knit communities from the follower-followee links. Still, the question of whether we can extract Twitter communities using both links and content is open. In this paper, we answer this question in the affirmative. Our model discovers coherent communities and topics, as evinced by qualitative results on sub-graphs of Twitter users. Furthermore, we evaluate our model on the task of predicting follower-followee links. We show that joint modeling of links and content significantly improves link prediction performance on a sub-graph of Twitter (consisting of about 0.7 million users and over 27 million tweets), compared to generative models based on only structure or only content and paths-based methods such as Katz.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129116816","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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