Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining最新文献
Recommending items to new or "cold-start" users is a challenging problem for recommender systems. Collaborative filtering approaches fail when the preference history of users is not available. A promising direction that has been explored recently [12] is to utilize the information in the social networks of users to improve the quality of cold-start recommendations. That is, given that users are part of a social network, a new user shows up in the network with no preference history and limited social links, the recommender system tries to learn the user's tastes as fast as possible. In this work, we model the learning of preferences of cold-start users using multi-armed bandits [5] embedded in a social network. We propose two novel strategies leveraging neighborhood estimates to improve the learning rate of bandits for cold-start users. Our first strategy, MixPair, combines estimates from pairs of neighboring bandits. It extends the well-known UCB1 algorithm [5] and inherits its asymptotically optimal guarantees. Although our second strategy, MixNeigh, is a heuristic based on consensus in the neighborhood of a user, it performed the best among the evaluated strategies. Our experiments on a dataset from Last.fm show that our strategies yield significant improvements, learning 2 to 5 times faster than our baseline, UCB1.
{"title":"Mixing bandits: a recipe for improved cold-start recommendations in a social network","authors":"S. Caron, Smriti Bhagat","doi":"10.1145/2501025.2501029","DOIUrl":"https://doi.org/10.1145/2501025.2501029","url":null,"abstract":"Recommending items to new or \"cold-start\" users is a challenging problem for recommender systems. Collaborative filtering approaches fail when the preference history of users is not available. A promising direction that has been explored recently [12] is to utilize the information in the social networks of users to improve the quality of cold-start recommendations. That is, given that users are part of a social network, a new user shows up in the network with no preference history and limited social links, the recommender system tries to learn the user's tastes as fast as possible.\u0000 In this work, we model the learning of preferences of cold-start users using multi-armed bandits [5] embedded in a social network. We propose two novel strategies leveraging neighborhood estimates to improve the learning rate of bandits for cold-start users. Our first strategy, MixPair, combines estimates from pairs of neighboring bandits. It extends the well-known UCB1 algorithm [5] and inherits its asymptotically optimal guarantees. Although our second strategy, MixNeigh, is a heuristic based on consensus in the neighborhood of a user, it performed the best among the evaluated strategies. Our experiments on a dataset from Last.fm show that our strategies yield significant improvements, learning 2 to 5 times faster than our baseline, UCB1.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"7 1","pages":"11:1-11:9"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84463365","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}
Fang Jin, Edward R. Dougherty, Parang Saraf, Yang Cao, Naren Ramakrishnan
Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.
{"title":"Epidemiological modeling of news and rumors on Twitter","authors":"Fang Jin, Edward R. Dougherty, Parang Saraf, Yang Cao, Naren Ramakrishnan","doi":"10.1145/2501025.2501027","DOIUrl":"https://doi.org/10.1145/2501025.2501027","url":null,"abstract":"Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"10 1","pages":"8:1-8:9"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88752093","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}
Community finding algorithms strive to find communities that have a higher connectivity within the communities than between them. Recently a framework called the community set space was introduced which provided a way to measure the quality of community sets. We present a new community finding algorithm, CHI, designed to minimize the violations defined by this framework. It will be shown that the CHI algorithm has similarities to kmeans. It is flexible and fast and can also be tuned to find certain types of communities. It is optimized for the community set framework and results so that it performs better than other algorithms within that framework.
{"title":"Community finding within the community set space","authors":"J. Scripps, C. Trefftz","doi":"10.1145/2501025.2501032","DOIUrl":"https://doi.org/10.1145/2501025.2501032","url":null,"abstract":"Community finding algorithms strive to find communities that have a higher connectivity within the communities than between them. Recently a framework called the community set space was introduced which provided a way to measure the quality of community sets. We present a new community finding algorithm, CHI, designed to minimize the violations defined by this framework. It will be shown that the CHI algorithm has similarities to kmeans. It is flexible and fast and can also be tuned to find certain types of communities. It is optimized for the community set framework and results so that it performs better than other algorithms within that framework.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"518 1","pages":"13:1-13:9"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77161785","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}
Recommender systems have proven to be a valuable tool for web companies like Amazon and Netflix for attracting and maintaining a large user base. However, in situations when user data is more scarce (e.g., for mid-sized companies, or an online retailer testing a new ratings system) algorithms tailored to smaller datasets can be used to further increase accuracy. This paper explores the potential of combining collaborative and content-based (using user comments) filtering algorithms using Yelp.com data from a single city. We present the method to combine two approaches, and find that the MSE for predicting a user's new rating can be reduced from a baseline MSE of 1.744 to 0.934 given just 2500 rated items in our real-world dataset.
{"title":"Customized reviews for small user-databases using iterative SVD and content based filtering","authors":"Jonathan Gregg, Nitin Jain","doi":"10.1145/2501025.2501036","DOIUrl":"https://doi.org/10.1145/2501025.2501036","url":null,"abstract":"Recommender systems have proven to be a valuable tool for web companies like Amazon and Netflix for attracting and maintaining a large user base. However, in situations when user data is more scarce (e.g., for mid-sized companies, or an online retailer testing a new ratings system) algorithms tailored to smaller datasets can be used to further increase accuracy. This paper explores the potential of combining collaborative and content-based (using user comments) filtering algorithms using Yelp.com data from a single city. We present the method to combine two approaches, and find that the MSE for predicting a user's new rating can be reduced from a baseline MSE of 1.744 to 0.934 given just 2500 rated items in our real-world dataset.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"12 1","pages":"14:1-14:5"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84277804","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}
Link prediction is used by many applications to recommend new products or social connections to people. Link prediction leverages information in network structure to identify missing links or predict which new one will form in the future. Recent research has provided a theoretical justification for the success of some popular link prediction heuristics, such as the number of common neighbors and the Adamic-Adar score, by showing that they estimate the distance between nodes in some latent feature space. In this paper we examine the link prediction task from the novel perspective of network flows. We show that how easily two nodes can interact with or influence each other depends not only on their position in the network, but also on the nature of the flow that mediates interactions between them. We show that different types of flows lead to different notions of network proximity, some of which are mathematically equivalent to existing link prediction heuristics. We measure the performance of different heuristics on the missing link prediction task in a variety of real-world social, technological and biological networks. We show that heuristics based on a random walk-type processes outperform the popular Adamic-Adar and the number of common neighbors heuristics in many networks.
{"title":"Network flows and the link prediction problem","authors":"Kanika Narang, Kristina Lerman, P. Kumaraguru","doi":"10.1145/2501025.2501031","DOIUrl":"https://doi.org/10.1145/2501025.2501031","url":null,"abstract":"Link prediction is used by many applications to recommend new products or social connections to people. Link prediction leverages information in network structure to identify missing links or predict which new one will form in the future. Recent research has provided a theoretical justification for the success of some popular link prediction heuristics, such as the number of common neighbors and the Adamic-Adar score, by showing that they estimate the distance between nodes in some latent feature space. In this paper we examine the link prediction task from the novel perspective of network flows. We show that how easily two nodes can interact with or influence each other depends not only on their position in the network, but also on the nature of the flow that mediates interactions between them. We show that different types of flows lead to different notions of network proximity, some of which are mathematically equivalent to existing link prediction heuristics. We measure the performance of different heuristics on the missing link prediction task in a variety of real-world social, technological and biological networks. We show that heuristics based on a random walk-type processes outperform the popular Adamic-Adar and the number of common neighbors heuristics in many networks.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"30 1","pages":"3:1-3:8"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74563444","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}
Chengfeng Lin, Jianhua He, Yi Zhou, Xiaokang Yang, Kai Chen, Li Song
Spamming has been a widespread problem for social networks. In recent years there is an increasing interest in the analysis of anti-spamming for microblogs, such as Twitter. In this paper we present a systematic research on the analysis of spamming in Sina Weibo platform, which is currently a dominant microblogging service provider in China. Our research objectives are to understand the specific spamming behaviors in Sina Weibo and find approaches to identify and block spammers in Sina Weibo based on spamming behavior classifiers. To start with the analysis of spamming behaviors we devise several effective methods to collect a large set of spammer samples, including uses of proactive honeypots and crawlers, keywords based searching and buying spammer samples directly from online merchants. We processed the database associated with these spammer samples and interestingly we found three representative spamming behaviors: aggressive advertising, repeated duplicate reposting and aggressive following. We extract various features and compare the behaviors of spammers and legitimate users with regard to these features. It is found that spamming behaviors and normal behaviors have distinct characteristics. Based on these findings we design an automatic online spammer identification system. Through tests with real data it is demonstrated that the system can effectively detect the spamming behaviors and identify spammers in Sina Weibo.
{"title":"Analysis and identification of spamming behaviors in Sina Weibo microblog","authors":"Chengfeng Lin, Jianhua He, Yi Zhou, Xiaokang Yang, Kai Chen, Li Song","doi":"10.1145/2501025.2501035","DOIUrl":"https://doi.org/10.1145/2501025.2501035","url":null,"abstract":"Spamming has been a widespread problem for social networks. In recent years there is an increasing interest in the analysis of anti-spamming for microblogs, such as Twitter. In this paper we present a systematic research on the analysis of spamming in Sina Weibo platform, which is currently a dominant microblogging service provider in China. Our research objectives are to understand the specific spamming behaviors in Sina Weibo and find approaches to identify and block spammers in Sina Weibo based on spamming behavior classifiers. To start with the analysis of spamming behaviors we devise several effective methods to collect a large set of spammer samples, including uses of proactive honeypots and crawlers, keywords based searching and buying spammer samples directly from online merchants. We processed the database associated with these spammer samples and interestingly we found three representative spamming behaviors: aggressive advertising, repeated duplicate reposting and aggressive following. We extract various features and compare the behaviors of spammers and legitimate users with regard to these features. It is found that spamming behaviors and normal behaviors have distinct characteristics. Based on these findings we design an automatic online spammer identification system. Through tests with real data it is demonstrated that the system can effectively detect the spamming behaviors and identify spammers in Sina Weibo.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"1 1","pages":"5:1-5:9"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76182607","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}
Stock is a popular topic in Twitter. The number of tweets concerning a stock varies over days, and sometimes exhibits a significant spike. In this paper, we investigate Twitter volume spikes related to S&P 500 stocks, and whether they are useful for stock trading. Through correlation analysis, we provide insight on when Twitter volume spikes occur and possible causes of these spikes. We further explore whether these spikes are surprises to market participants by comparing the implied volatility of a stock before and after a Twitter volume spike. Moreover, we develop a Bayesian classifier that uses Twitter volume spikes to assist stock trading, and show that it can provide substantial profit. We further develop an enhanced strategy that combines the Bayesian classifier and a stock bottom picking method, and demonstrate that it can achieve significant gain in a short amount of time. Simulation over a half year's stock market data indicates that it achieves on average 8.6% gain in 27 trading days and 15.0% gain in 55 trading days. Statistical tests show that the gain is statistically significant, and the enhanced strategy significantly outperforms the strategy that only uses the Bayesian classifier as well as a bottom picking method that uses trading volume spikes.
{"title":"Twitter volume spikes: analysis and application in stock trading","authors":"Yuexin Mao, Wei Wei, B. Wang","doi":"10.1145/2501025.2501039","DOIUrl":"https://doi.org/10.1145/2501025.2501039","url":null,"abstract":"Stock is a popular topic in Twitter. The number of tweets concerning a stock varies over days, and sometimes exhibits a significant spike. In this paper, we investigate Twitter volume spikes related to S&P 500 stocks, and whether they are useful for stock trading. Through correlation analysis, we provide insight on when Twitter volume spikes occur and possible causes of these spikes. We further explore whether these spikes are surprises to market participants by comparing the implied volatility of a stock before and after a Twitter volume spike. Moreover, we develop a Bayesian classifier that uses Twitter volume spikes to assist stock trading, and show that it can provide substantial profit. We further develop an enhanced strategy that combines the Bayesian classifier and a stock bottom picking method, and demonstrate that it can achieve significant gain in a short amount of time. Simulation over a half year's stock market data indicates that it achieves on average 8.6% gain in 27 trading days and 15.0% gain in 55 trading days. Statistical tests show that the gain is statistically significant, and the enhanced strategy significantly outperforms the strategy that only uses the Bayesian classifier as well as a bottom picking method that uses trading volume spikes.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"8 1","pages":"4:1-4:9"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80783528","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}
A. Silva, Sara Guimarães, Wagner Meira Jr, Mohammed J. Zaki
Understanding information diffusion processes that take place on the Web, specially in social media, is a fundamental step towards the design of effective information diffusion mechanisms, recommendation systems, and viral marketing/advertising campaigns. Two key concepts in information diffusion are influence and relevance. Influence is the ability to popularize content in an online community. To this end, influentials introduce and propagate relevant content, in the sense that such content satisfies the information needs of a significant portion of this community. In this paper, we study the problem of identifying influential users and relevant content in information diffusion data. We propose ProfileRank, a new information diffusion model based on random walks over a user-content graph. ProfileRank is a PageRank inspired model that exploits the principle that relevant content is created and propagated by influential users and influential users create relevant content. A convenient property of ProfileRank is that it can be adapted to provide personalized recommendations. Experimental results demonstrate that ProfileRank makes accurate recommendations, outperforming baseline techniques. We also illustrate relevant content and influential users discovered using ProfileRank. Our analysis shows that ProfileRank scores are more correlated with content diffusion than with the network structure. We also show that our new modeling is more efficient than PageRank to perform these calculations.
{"title":"ProfileRank: finding relevant content and influential users based on information diffusion","authors":"A. Silva, Sara Guimarães, Wagner Meira Jr, Mohammed J. Zaki","doi":"10.1145/2501025.2501033","DOIUrl":"https://doi.org/10.1145/2501025.2501033","url":null,"abstract":"Understanding information diffusion processes that take place on the Web, specially in social media, is a fundamental step towards the design of effective information diffusion mechanisms, recommendation systems, and viral marketing/advertising campaigns. Two key concepts in information diffusion are influence and relevance. Influence is the ability to popularize content in an online community. To this end, influentials introduce and propagate relevant content, in the sense that such content satisfies the information needs of a significant portion of this community.\u0000 In this paper, we study the problem of identifying influential users and relevant content in information diffusion data. We propose ProfileRank, a new information diffusion model based on random walks over a user-content graph. ProfileRank is a PageRank inspired model that exploits the principle that relevant content is created and propagated by influential users and influential users create relevant content. A convenient property of ProfileRank is that it can be adapted to provide personalized recommendations.\u0000 Experimental results demonstrate that ProfileRank makes accurate recommendations, outperforming baseline techniques. We also illustrate relevant content and influential users discovered using ProfileRank. Our analysis shows that ProfileRank scores are more correlated with content diffusion than with the network structure. We also show that our new modeling is more efficient than PageRank to perform these calculations.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"28 1","pages":"2:1-2:9"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79345648","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}
Given that users are simultaneously connected in multiple communication channels in a social networking service site (e.g., chat, message, and group message), we explore user's collective networking behavior. We collected the data from a mobile social networking site with 4.8 million registered users. The empirical estimation shows interesting results: (1) there are cross-effects across the communication channels: substitute effects for "chat and message" and complementary effects for "message and group message" and "chat and group message" (2) there is significant local network effect but global network effect is not observed, (3) users utilize communication channels for different purposes according to their networking activity level (conveying simple information vs. building sophisticated inter-relationship), and (4) we identify the distinct evolutionary trajectories of an individual user's networking behavior by channel: negative slopes for chat and message vs. upward trend for a group message. Our experimental study shows that we can better predict the word of mouth (WOM) effects by understanding users' collective networking behavior across diverse channels.
{"title":"The user's communication patterns on a mobile social network site","authors":"Youngsoo Kim","doi":"10.1145/2501025.2501037","DOIUrl":"https://doi.org/10.1145/2501025.2501037","url":null,"abstract":"Given that users are simultaneously connected in multiple communication channels in a social networking service site (e.g., chat, message, and group message), we explore user's collective networking behavior. We collected the data from a mobile social networking site with 4.8 million registered users. The empirical estimation shows interesting results: (1) there are cross-effects across the communication channels: substitute effects for \"chat and message\" and complementary effects for \"message and group message\" and \"chat and group message\" (2) there is significant local network effect but global network effect is not observed, (3) users utilize communication channels for different purposes according to their networking activity level (conveying simple information vs. building sophisticated inter-relationship), and (4) we identify the distinct evolutionary trajectories of an individual user's networking behavior by channel: negative slopes for chat and message vs. upward trend for a group message. Our experimental study shows that we can better predict the word of mouth (WOM) effects by understanding users' collective networking behavior across diverse channels.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"67 1","pages":"12:1-12:6"},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85791903","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}
Pub Date : 2010-01-01DOI: 10.1007/978-3-642-14929-0
Snakdd, C. Lee Giles
{"title":"Advances in Social Network Mining and Analysis, Second International Workshop, SNAKDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers","authors":"Snakdd, C. Lee Giles","doi":"10.1007/978-3-642-14929-0","DOIUrl":"https://doi.org/10.1007/978-3-642-14929-0","url":null,"abstract":"","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83731609","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}
Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining
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