Pub Date : 2013-10-08DOI: 10.1109/CASoN.2013.6622608
S. Tanbeer, Fan Jiang, C. Leung, Richard Kyle MacKinnon, Irish J. M. Medina
Social networking websites such as Facebook, LinkedIn, Twitter, and Weibo have been used for collaboration and knowledge sharing between users. The mining of social network data has become an important topic in data mining and computational aspects of social networks. Nowadays, it is not uncommon for most users in a social network to have many friends and in multiple social domains. Among these friends, some groups of friends are more significant than others. In this paper, we introduce a data mining technique that helps social network users find groups of friends who are significant across multiple domains in social networks.
{"title":"Finding groups of friends who are significant across multiple domains in social networks","authors":"S. Tanbeer, Fan Jiang, C. Leung, Richard Kyle MacKinnon, Irish J. M. Medina","doi":"10.1109/CASoN.2013.6622608","DOIUrl":"https://doi.org/10.1109/CASoN.2013.6622608","url":null,"abstract":"Social networking websites such as Facebook, LinkedIn, Twitter, and Weibo have been used for collaboration and knowledge sharing between users. The mining of social network data has become an important topic in data mining and computational aspects of social networks. Nowadays, it is not uncommon for most users in a social network to have many friends and in multiple social domains. Among these friends, some groups of friends are more significant than others. In this paper, we introduce a data mining technique that helps social network users find groups of friends who are significant across multiple domains in social networks.","PeriodicalId":221487,"journal":{"name":"2013 Fifth International Conference on Computational Aspects of Social Networks","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121424723","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 : 2013-10-08DOI: 10.1109/CASoN.2013.6622606
L. Qian, Lei Fan, Jianhua Li
Online social networks like Weibo and Twitter consist of billions of users and connections, and traditional approaches which are based on serial algorithms and leveraged only a single node or even a single core cannot suffice the that scale of data any more. We propose new distributed quasi-parallel breadth-first search scheme, the common graph traversal algorithm, based on the MapReduce framework, which has better performance (up to one scale of magnitude less time complexity for single-source cases or even better for multiple-source cases) than Pegasus, the state-of-the-art graph mining library, in terms of the complexity of computation and the I/O load. We apply our algorithms on the Weibo dataset, crawled from its website, which contains 135 million users and 10.2 billion directed connections among them, and occupies up to 400 gigabytes. The dataset is by far the largest one of online social networks in research. Based on the Weibo dataset with extremely skewed degree distribution, we give the empirical time complexity and I/O load analysis in each iteration of our proposed methods. Also, We ran the experiments on a 20-node Hadoop cluster to validate our analysis, and the results conform to our predicted empirical results.
{"title":"Implementing quasi-parallel breadth-first search in MapReduce for large-scale social network mining","authors":"L. Qian, Lei Fan, Jianhua Li","doi":"10.1109/CASoN.2013.6622606","DOIUrl":"https://doi.org/10.1109/CASoN.2013.6622606","url":null,"abstract":"Online social networks like Weibo and Twitter consist of billions of users and connections, and traditional approaches which are based on serial algorithms and leveraged only a single node or even a single core cannot suffice the that scale of data any more. We propose new distributed quasi-parallel breadth-first search scheme, the common graph traversal algorithm, based on the MapReduce framework, which has better performance (up to one scale of magnitude less time complexity for single-source cases or even better for multiple-source cases) than Pegasus, the state-of-the-art graph mining library, in terms of the complexity of computation and the I/O load. We apply our algorithms on the Weibo dataset, crawled from its website, which contains 135 million users and 10.2 billion directed connections among them, and occupies up to 400 gigabytes. The dataset is by far the largest one of online social networks in research. Based on the Weibo dataset with extremely skewed degree distribution, we give the empirical time complexity and I/O load analysis in each iteration of our proposed methods. Also, We ran the experiments on a 20-node Hadoop cluster to validate our analysis, and the results conform to our predicted empirical results.","PeriodicalId":221487,"journal":{"name":"2013 Fifth International Conference on Computational Aspects of Social Networks","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116710206","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 : 2013-10-08DOI: 10.1109/CASoN.2013.6622605
G. Dziczkowski, K. Wegrzyn-Wolska, L. Bougueroua
This paper describes functions of a system designed for the behavior analysis of e-commerce clients. It enables user identification and client behavior extraction for interacting with web site customers. General approaches used in the field of Web Usage Mining are presented together with proposals to extend the data base with the information gained from e-commerce site forums and queries. Our system carries out an evaluation and rating of opinions, and our approach is based on linguistic and the statistic treatment of natural language. Three different methods for classifying opinions from clients' forum are used, and two new methods, based on linguistic knowledge to assign a mark dependent upon the client's emotions and opinions described in forum comments, have been introduced.
{"title":"An opinion mining approach for web user identification and clients' behaviour analysis","authors":"G. Dziczkowski, K. Wegrzyn-Wolska, L. Bougueroua","doi":"10.1109/CASoN.2013.6622605","DOIUrl":"https://doi.org/10.1109/CASoN.2013.6622605","url":null,"abstract":"This paper describes functions of a system designed for the behavior analysis of e-commerce clients. It enables user identification and client behavior extraction for interacting with web site customers. General approaches used in the field of Web Usage Mining are presented together with proposals to extend the data base with the information gained from e-commerce site forums and queries. Our system carries out an evaluation and rating of opinions, and our approach is based on linguistic and the statistic treatment of natural language. Three different methods for classifying opinions from clients' forum are used, and two new methods, based on linguistic knowledge to assign a mark dependent upon the client's emotions and opinions described in forum comments, have been introduced.","PeriodicalId":221487,"journal":{"name":"2013 Fifth International Conference on Computational Aspects of Social Networks","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124683838","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 : 2013-10-08DOI: 10.1109/CASoN.2013.6622597
Martin Kopka, M. Kudelka, Jakub Stolfa, Ondrej Kobersky, V. Snás̃el
Information systems support and ensure the practical running of most critical business processes. There exists or can be reconstructed a record (log) of the process running in the information system with information about the participants and the processed objects for most of the processes. This research was realized in the environment of the enterprise information system SAP. Participants of business processes stand in different relationships. We are interested in the relationships that are not explicitly seen from the process logs, but which are detectable by research methods of social networks and communities in social networks. Our work constructs the social network from the process log in the given context and then it finds communities in this network. Found communities were analyzed using knowledge of the business process and the environment in which the process operates. We found that identified communities have reasonable representation in the actual process, and this opened up a new dimension of knowledge that can be analyzed from the process log. This approach seems to be promising for detailed analysis.
{"title":"Extraction and analysis social networks from process data","authors":"Martin Kopka, M. Kudelka, Jakub Stolfa, Ondrej Kobersky, V. Snás̃el","doi":"10.1109/CASoN.2013.6622597","DOIUrl":"https://doi.org/10.1109/CASoN.2013.6622597","url":null,"abstract":"Information systems support and ensure the practical running of most critical business processes. There exists or can be reconstructed a record (log) of the process running in the information system with information about the participants and the processed objects for most of the processes. This research was realized in the environment of the enterprise information system SAP. Participants of business processes stand in different relationships. We are interested in the relationships that are not explicitly seen from the process logs, but which are detectable by research methods of social networks and communities in social networks. Our work constructs the social network from the process log in the given context and then it finds communities in this network. Found communities were analyzed using knowledge of the business process and the environment in which the process operates. We found that identified communities have reasonable representation in the actual process, and this opened up a new dimension of knowledge that can be analyzed from the process log. This approach seems to be promising for detailed analysis.","PeriodicalId":221487,"journal":{"name":"2013 Fifth International Conference on Computational Aspects of Social Networks","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129054944","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 : 2013-10-08DOI: 10.1109/CASoN.2013.6622598
S. Zehnalova, Z. Horak, M. Kudelka, V. Snás̃el
In social networks the participants may be characterized by their roles. We understand roles as different patterns of link structure in the network. These roles describe the node and its activity in the network over time. Self-organizing maps (SOMs) - type of artificial neural-networks, are used for node's role identification and for discovery of all the roles present in the network. Different data preprocessing methods allow us to capture different aspects of roles. We show results of the experiment with a large scale co-authorship network constructed from a DBLP dataset.
{"title":"Using self-organizing maps for identification of roles in social networks","authors":"S. Zehnalova, Z. Horak, M. Kudelka, V. Snás̃el","doi":"10.1109/CASoN.2013.6622598","DOIUrl":"https://doi.org/10.1109/CASoN.2013.6622598","url":null,"abstract":"In social networks the participants may be characterized by their roles. We understand roles as different patterns of link structure in the network. These roles describe the node and its activity in the network over time. Self-organizing maps (SOMs) - type of artificial neural-networks, are used for node's role identification and for discovery of all the roles present in the network. Different data preprocessing methods allow us to capture different aspects of roles. We show results of the experiment with a large scale co-authorship network constructed from a DBLP dataset.","PeriodicalId":221487,"journal":{"name":"2013 Fifth International Conference on Computational Aspects of Social Networks","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132660187","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 : 2013-10-08DOI: 10.1109/CASoN.2013.6622599
R. Bedi, Nitinkumar Rajendra Gove, V. Wadhai
Social network is a network of people spread across all over the globe. Each social network user has a profile, which stores user's personal information, his likes, interests etc. The number of social network users is growing exponentially, every day. This makes social network an ultimate repository of large user data and an important live information source. The large information available over social network attracts the attention of business, corporate and marketing people. So, these people try mining the user data/profile through different ways. Also, as most of the user profiles are publicly visible, it is very easy to obtain a particular user's information without his concern. This leads to a privacy breach causing leakage of user's private information, without even a hint of it to the user. We studied 100 facebook live user's profiles and facebook privacy policy, to understand the privacy awareness in facebook users. In this paper, we present results of the surveys conducted in this study. We, further, propose a new generic framework named `Hippocratic Social Network', to enhance the personal level privacy in facebook and other online social networking sites.
{"title":"Hippocratic social network","authors":"R. Bedi, Nitinkumar Rajendra Gove, V. Wadhai","doi":"10.1109/CASoN.2013.6622599","DOIUrl":"https://doi.org/10.1109/CASoN.2013.6622599","url":null,"abstract":"Social network is a network of people spread across all over the globe. Each social network user has a profile, which stores user's personal information, his likes, interests etc. The number of social network users is growing exponentially, every day. This makes social network an ultimate repository of large user data and an important live information source. The large information available over social network attracts the attention of business, corporate and marketing people. So, these people try mining the user data/profile through different ways. Also, as most of the user profiles are publicly visible, it is very easy to obtain a particular user's information without his concern. This leads to a privacy breach causing leakage of user's private information, without even a hint of it to the user. We studied 100 facebook live user's profiles and facebook privacy policy, to understand the privacy awareness in facebook users. In this paper, we present results of the surveys conducted in this study. We, further, propose a new generic framework named `Hippocratic Social Network', to enhance the personal level privacy in facebook and other online social networking sites.","PeriodicalId":221487,"journal":{"name":"2013 Fifth International Conference on Computational Aspects of Social Networks","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124087512","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 : 2013-10-08DOI: 10.1109/CASoN.2013.6622595
Prachi Deshpande, Aditi Aggarwal, S.C. Sharma, P.Sateesh Kumar, A. Abraham
Cloud Computing is becoming a promising technology for processing a huge chunk of data. Hence, its security aspect has drawn the attentions of researchers and academician. The security of the cloud environment must be reliable as well as scalable. The cloud environment is vulnerable to many security attacks. Attacks can be launched individually or in tandem. In this article, the overview of port-scan attack and the response of IDS are studied. The experimentation is carried out using virtual-box and SNORT, the open-source IDS.
{"title":"Distributed port-scan attack in cloud environment","authors":"Prachi Deshpande, Aditi Aggarwal, S.C. Sharma, P.Sateesh Kumar, A. Abraham","doi":"10.1109/CASoN.2013.6622595","DOIUrl":"https://doi.org/10.1109/CASoN.2013.6622595","url":null,"abstract":"Cloud Computing is becoming a promising technology for processing a huge chunk of data. Hence, its security aspect has drawn the attentions of researchers and academician. The security of the cloud environment must be reliable as well as scalable. The cloud environment is vulnerable to many security attacks. Attacks can be launched individually or in tandem. In this article, the overview of port-scan attack and the response of IDS are studied. The experimentation is carried out using virtual-box and SNORT, the open-source IDS.","PeriodicalId":221487,"journal":{"name":"2013 Fifth International Conference on Computational Aspects of Social Networks","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122846171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Social Network Services have become an important medium for people to communicate ideas and share interests in recent years. Blogs published and shared by users in this virtual world are one of the main sources of user-generated information. Classifying these freestyle blogs can help understand user interests and assist applications such as search and marketing. In this paper, we propose a new method of multi-label classification for Chinese blogs. By applying Dempster-Shafer theory on semantic word similarity algorithms, we achieve automatic classification without use of difficult-to-obtain training sets. Experiments were conducted on real world data from RENREN.com, the biggest SNS (Social Network Services) in China. Results show that the proposed method achieves satisfactory performance in multi-labeling real world SNS blogs as well as corpus.
{"title":"Chinese SNS blog classification using semantic similarity","authors":"Chenye Shi, Jianhua Li, Jieyuan Chen, Xiuzhen Chen","doi":"10.1109/CASON.2013.6622603","DOIUrl":"https://doi.org/10.1109/CASON.2013.6622603","url":null,"abstract":"Social Network Services have become an important medium for people to communicate ideas and share interests in recent years. Blogs published and shared by users in this virtual world are one of the main sources of user-generated information. Classifying these freestyle blogs can help understand user interests and assist applications such as search and marketing. In this paper, we propose a new method of multi-label classification for Chinese blogs. By applying Dempster-Shafer theory on semantic word similarity algorithms, we achieve automatic classification without use of difficult-to-obtain training sets. Experiments were conducted on real world data from RENREN.com, the biggest SNS (Social Network Services) in China. Results show that the proposed method achieves satisfactory performance in multi-labeling real world SNS blogs as well as corpus.","PeriodicalId":221487,"journal":{"name":"2013 Fifth International Conference on Computational Aspects of Social Networks","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124818409","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 : 2013-10-08DOI: 10.1109/CASoN.2013.6622601
J. Cruz, Cécile Bothorel
Real social networks can be described using two dimensions: first a structural dimension that contains the social graph, e.g. the actors and the relationships between them, and second a compositional dimension containing the actors' attributes, e.g. their profile. Each of these dimensions can be used independently to cluster the nodes and explain different phenomena occurring on the social network, whether from a connectivity or an individual perspective. In the case of community detection problem, an emergent research field explores how to include relationships and node attributes in an integrated clustering process. In this paper, we present a novel approach which integrate two partitions, one structural and one compositional, after they habe been generated by dedicated and specialized clustering steps. We rely on a contingency matrix with structural groups in rows and compositional ones in columns. The problem is to manipulate rows and columns to provide a new partition which maintains a good trade-off between both dimensions. In this paper we propose two strategies to control the combination. Tested on real-world social networks, the final partitions are evaluated in terms of entropy and density, and compared to pure structural or compositional partitions. The unified partitions show interesting properties, such as cohesive and homogeneous groups of actors. The method offers fine control on the combination process, giving new search capabilities to analysts without requiring the re-computation of the partitions.
{"title":"Information integration for detecting communities in attributed graphs","authors":"J. Cruz, Cécile Bothorel","doi":"10.1109/CASoN.2013.6622601","DOIUrl":"https://doi.org/10.1109/CASoN.2013.6622601","url":null,"abstract":"Real social networks can be described using two dimensions: first a structural dimension that contains the social graph, e.g. the actors and the relationships between them, and second a compositional dimension containing the actors' attributes, e.g. their profile. Each of these dimensions can be used independently to cluster the nodes and explain different phenomena occurring on the social network, whether from a connectivity or an individual perspective. In the case of community detection problem, an emergent research field explores how to include relationships and node attributes in an integrated clustering process. In this paper, we present a novel approach which integrate two partitions, one structural and one compositional, after they habe been generated by dedicated and specialized clustering steps. We rely on a contingency matrix with structural groups in rows and compositional ones in columns. The problem is to manipulate rows and columns to provide a new partition which maintains a good trade-off between both dimensions. In this paper we propose two strategies to control the combination. Tested on real-world social networks, the final partitions are evaluated in terms of entropy and density, and compared to pure structural or compositional partitions. The unified partitions show interesting properties, such as cohesive and homogeneous groups of actors. The method offers fine control on the combination process, giving new search capabilities to analysts without requiring the re-computation of the partitions.","PeriodicalId":221487,"journal":{"name":"2013 Fifth International Conference on Computational Aspects of Social Networks","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127918946","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 : 2013-10-08DOI: 10.1109/CASoN.2013.6622602
Derek Doran, Huda Alhazmi, S. Gokhale
Most computational techniques that analyze Online Social Networks (OSNs) aim to discover patterns in a network's structure and the behavior of its users, but do not seek to understand how people's motives lead to these patterns. Studying the social effects that cause these patterns, however, can produce deeper insights that may transcend a specific network and are generically applicable. Therefore, a more promising approach is to anchor computational techniques to the underlying social effects that can explain the reasons behind why users interact the way they do. In this paper, we discover how the social effects of stature, relationship strength, and egocentricity shape the interactions among Facebook users. These effects are explored through transitivity in triads, which are network units that capture dynamics among triples of users. The analysis suggests that Facebook interactions are influenced by users with concentrated stature and strong bonds. However, the activities of popular and over-active users have little influence.
大多数分析在线社交网络(Online Social Networks, OSNs)的计算技术旨在发现网络结构和用户行为中的模式，但并不试图理解人们的动机如何导致这些模式。然而，研究导致这些模式的社会影响可以产生更深入的见解，这些见解可能超越特定的网络，并且具有普遍适用性。因此，一个更有前途的方法是将计算技术锚定在潜在的社会效应上，这可以解释为什么用户以他们的方式交互背后的原因。在本文中，我们发现身高、关系强度和自我中心的社会效应如何塑造Facebook用户之间的互动。这些影响是通过三元组中的传递性来探索的，三元组是捕捉三元组用户之间动态的网络单元。分析表明，Facebook上的互动受到高度集中、联系紧密的用户的影响。然而，流行用户和过度活跃用户的活动几乎没有影响。
{"title":"Triads, transitivity, and social effects in user interactions on Facebook","authors":"Derek Doran, Huda Alhazmi, S. Gokhale","doi":"10.1109/CASoN.2013.6622602","DOIUrl":"https://doi.org/10.1109/CASoN.2013.6622602","url":null,"abstract":"Most computational techniques that analyze Online Social Networks (OSNs) aim to discover patterns in a network's structure and the behavior of its users, but do not seek to understand how people's motives lead to these patterns. Studying the social effects that cause these patterns, however, can produce deeper insights that may transcend a specific network and are generically applicable. Therefore, a more promising approach is to anchor computational techniques to the underlying social effects that can explain the reasons behind why users interact the way they do. In this paper, we discover how the social effects of stature, relationship strength, and egocentricity shape the interactions among Facebook users. These effects are explored through transitivity in triads, which are network units that capture dynamics among triples of users. The analysis suggests that Facebook interactions are influenced by users with concentrated stature and strong bonds. However, the activities of popular and over-active users have little influence.","PeriodicalId":221487,"journal":{"name":"2013 Fifth International Conference on Computational Aspects of Social Networks","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121572739","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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