Results 1 - 10
of
19
Community Detection with Edge Content in Social Media Networks
"... Abstract—The problem of community detection in social media has been widely studied in the social networking community in the context of the structure of the underlying graphs. Most community detection algorithms use the links between the nodes in order to determine the dense regions in the graph. T ..."
Abstract
-
Cited by 8 (2 self)
- Add to MetaCart
(Show Context)
Abstract—The problem of community detection in social media has been widely studied in the social networking community in the context of the structure of the underlying graphs. Most community detection algorithms use the links between the nodes in order to determine the dense regions in the graph. These dense regions are the communities of social media in the graph. Such methods are typically based purely on the linkage structure of the underlying social media network. However, in many recent applications, edge content is available in order to provide better supervision to the community detection process. Many natural representations of edges in social interactions such as shared images and videos, user tags and comments are naturally associated with content on the edges. While some work has been done on utilizing node content for community detection, the presence of edge content presents unprecedented opportunities and flexibility for the community detection process. We will show that such edge content can be leveraged in order to greatly improve the effectiveness of the community detection process in social media networks. We present experimental results illustrating the effectiveness of our approach. Index Terms—ignore I.
Towards Community Detection in Locally Heterogeneous Networks
"... In recent years, the size of many social networks such as Facebook, MySpace, andLinkedIn has exploded at a rapid pace, because of its convenience in using the internet in order to connect geographically disparate users. This has lead to considerable interest in many graph-theoretical aspects of soci ..."
Abstract
-
Cited by 8 (4 self)
- Add to MetaCart
(Show Context)
In recent years, the size of many social networks such as Facebook, MySpace, andLinkedIn has exploded at a rapid pace, because of its convenience in using the internet in order to connect geographically disparate users. This has lead to considerable interest in many graph-theoretical aspects of social networks such as the underlying communities, the graph diameter, and other structural information which can be used in order to mine useful information from the social network. The graph structure of social networks is influenced by the underlying social behavior, which can vary considerably over different groups of individuals. One of the disadvantages of existing schemes is that they attempt to determine global communities, which (implicitly) assume uniform behavior over the network. This is not very well suited to the differences in the underlying density in different regions of the social network. As a result, a global analysis over social community structure can result in either very small communities (in sparse regions), or communities which are too large and incoherent (in dense regions). In order to handle the challenge of local heterogeneity, we will explore a simple property of social networks, which we refer to as the local succinctness property. We will use this property in order to extract compressed descriptions of the underlying community representation of the social network with the use of a min-hash approach. We will show that this approach creates balanced communities across a heterogeneous network in an effective way. We apply the approach to a variety of data sets, and illustrate its effectiveness over competing techniques.
Different Approaches to Groups and Key Person Identification in Blogosphere
"... Abstract—Two approaches to key person identification in the blogosphere-based social network are analysed in the paper: discovery of most important individuals either in persistent or in global social communities existing on web blogs. A new method for separation of stable groups fulfilling given co ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
(Show Context)
Abstract—Two approaches to key person identification in the blogosphere-based social network are analysed in the paper: discovery of most important individuals either in persistent or in global social communities existing on web blogs. A new method for separation of stable groups fulfilling given conditions is presented. Additionally, a new concept for extraction of user roles and key persons in such groups is proposed. It has been compared to the general clustering method and structural node position measure applied to rank users in the time-aggregated data. Experimental, comparative studies have been conducted on real blogosphere data gathered over one year. Keywords-social network, social network analysis, SNA, social group extraction, key person, persistent role identification, blogosphere, stable group extraction, CPM, fast modularity optimization, node position I.
Social networks and statistical relational learning: a survey
"... Abstract: One of the most appreciated functionality of computers nowadays is their being a means for communication and information sharing among people. With the spread of the internet, several complex interactions have taken place among people, giving rise to huge information networks based on thes ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
Abstract: One of the most appreciated functionality of computers nowadays is their being a means for communication and information sharing among people. With the spread of the internet, several complex interactions have taken place among people, giving rise to huge information networks based on these interactions. Social networks potentially represent an invaluable source of information that can be exploited for scientific and commercial purposes. On the other hand, due to their distinguishing peculiarities (huge size and inherent relational setting) with respect to all previous information extraction tasks faced in computer science, they require new techniques to gather this information. Social network mining (SNM) is the corresponding research area, aimed at extracting information about the network objects and behaviour that cannot be obtained based on the explicit/implicit description of the objects alone, ignoring their explicit/implicit relationships. Statistical relational learning (SRL) is a very promising approach to SNM, since it combines expressive representation formalisms, able to model complex relational networks, with statistical methods able to handle uncertainty about objects and relations. This paper is a survey of some SRL formalisms and techniques adopted to solve some SNM tasks.
Highlighting Stakeholder Communities to Support Requirements Decision-Making?
"... Abstract. [Context & motivation] Stakeholders participation is recognized as a key issue in the development of useful and usable systems. The Web has given rise to a growing number of collaborative working tools that facilitated the partic-ipation of stakeholders (and especially end-users). Thes ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract. [Context & motivation] Stakeholders participation is recognized as a key issue in the development of useful and usable systems. The Web has given rise to a growing number of collaborative working tools that facilitated the partic-ipation of stakeholders (and especially end-users). These tools create new oppor-tunities of practice regarding requirement elicitation. [Question/problem] Nev-ertheless, they result in an information overload lacking structure and semantics. Consequently, requirements analysis and selection becomes more challenging. [Principal ideas/results] In this paper, we propose an approach based on se-mantic web languages as well as concept lattices to identify relevant groups of stakeholders depending on their past participation. [Contribution] These groups can be used to enable facilitated decision-making and handling of requirements. We detail the different steps and the possible configurations, using an example inspired by a collaborative software development environment.
Key Person Analysis in Social Communities within the Blogosphere
"... Abstract: Identifying key persons active in social groups in the blogosphere is performed by means of social network analysis. Two main independent approaches are considered in the paper: (i) discovery of the most important individuals in persistent social communities and (ii) regular centrality me ..."
Abstract
- Add to MetaCart
Abstract: Identifying key persons active in social groups in the blogosphere is performed by means of social network analysis. Two main independent approaches are considered in the paper: (i) discovery of the most important individuals in persistent social communities and (ii) regular centrality measures applied either to social groups or the entire network. A new method for separating of groups stable over time, fulfilling given conditions of activity level of their members is proposed. Furthermore, a new concept for extracting user roles and key persons in such groups is also presented. This new approach was compared to the typical clustering method and the structural node position measure applied to rank users. The experimental studies have been carried out on real two-year blogosphere data.
Community-Contributed Media Collections: Knowledge at Our Fingertips
"... Abstract The widespread popularity of the Web has supported collaborative efforts to build large collections of community-contributed media. For example, social video-sharing communities like YouTube are incorporating ever-increasing amounts of user-contributed media, or photo-sharing communities l ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract The widespread popularity of the Web has supported collaborative efforts to build large collections of community-contributed media. For example, social video-sharing communities like YouTube are incorporating ever-increasing amounts of user-contributed media, or photo-sharing communities like Flickr are managing a huge photographic database at a large scale. The variegated abundance of multimodal, user-generated material opens new and exciting research perspectives and contextually introduces novel challenges. This chapter reviews different collections of user-contributed media, such as YouTube, Flickr, and Wikipedia, by presenting the main features of their online social networking sites. Different research efforts related to community-contributed media collections are presented and discussed. The works described in this chapter aim to (a) improve the automatic understanding of this multimedia data and (b) enhance the document classification task and the user searching activity on media collections.
The impact of unlinkability on adversarial community detection: effects and countermeasures
"... Abstract. We consider the threat model of a mobile-adversary drawn from contemporary computer security literature, and explore the dynamics of community detection and hiding in this setting. Using a real-world social network, we examine the extent of network topology information an adversary is requ ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract. We consider the threat model of a mobile-adversary drawn from contemporary computer security literature, and explore the dynamics of community detection and hiding in this setting. Using a real-world social network, we examine the extent of network topology information an adversary is required to gather in order to accurately ascertain community membership information. We show that selective surveillance strategies can improve the adversary’s efficiency over random wiretapping. We then consider possible privacy preserving defenses; using anonymous communications helps, but not much; however, the use of counter-surveillance techniques can significantly reduce the adversary’s ability to learn community membership. Our analysis shows that even when using anonymous communications an adversary placing a selectively chosen 8 % of the nodes of this network under surveillance (using key-logger probes) can de-anonymize the community membership of as much as 50 % of the network. Uncovering all community information with targeted selection requires probing as much as 75 % of the network. Finally, we show that a privacy conscious community can substantially disrupt community detection using only local knowledge even while facing up to the asymmetry of a completely knowledgeable mobile-adversary. 1
Profiling Social Network Users with Machine Learning
"... Social Networks are becoming increasingly popular in our daily communication and rapid developments in data gathering technology have led to large amounts of data that are available from users ’ interactions. On the other side, the complexity of analyzing social interactions is not related only to t ..."
Abstract
- Add to MetaCart
(Show Context)
Social Networks are becoming increasingly popular in our daily communication and rapid developments in data gathering technology have led to large amounts of data that are available from users ’ interactions. On the other side, the complexity of analyzing social interactions is not related only to the size of the network to be analyzed, but also to the nature of the interactions. One of the most important tasks in analyzing social networks is the user profiling (or clustering) which makes possible to design customized marketing strategies based on the type of the user. A user falling under a certain profile could then target with the same products used for other users in the same group. In this context, it is important to develop approaches that are able to efficiently and effectively profile users based on their interactions with other users. Machine learning methods have shown the capability to automatically discover patterns from data even in scenarios where complex relationships holds. In this paper, we show through experiments how machine learning algorithms can be effectively used to produce accurate profiling of real-world social network users. We show that users can be clustered in groups and that interesting patterns can be discovered among users not directly linked with decision tree learning.
Clustering online social network communities using genetic algorithms
"... To analyze the activities in an Online Social Network (OSN), we introduce the concept of “Node of Attraction ” (NoA) which represents the most active node in a network community. This NoA is identified as the origin/initiator of a post/communication which attracted other nodes and formed a cluster a ..."
Abstract
- Add to MetaCart
To analyze the activities in an Online Social Network (OSN), we introduce the concept of “Node of Attraction ” (NoA) which represents the most active node in a network community. This NoA is identified as the origin/initiator of a post/communication which attracted other nodes and formed a cluster at any point in time. In this research, a genetic algorithm (GA) is used as a data mining method where the main objective is to determine clusters of network communities in a given OSN dataset. This approach is efficient in handling different type of discussion topics in our studied OSN –comments, emails, chat sessions, etc. and can form clusters according to one or more topics. We believe that this work can be useful in finding the source for spread of rumors/misinformation, help law enforcement in resource allocation in crowd management, etc. The paper presents this GAbased clustering of online interactions and reports some results of experiments with real-world data and demonstrates the performance of proposed approach.
