Detecting community structure in social networks is a fundamental problem empowering us to identify groups of actors with similar interests. There have been extensive works focusing on finding communities in static networks, however, in reality, due to dynamic nature of social networks, they are evolving continuously. Ignoring the dynamic aspect of social networks, neither allows us to capture ...

We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, they are extremely hard to be uncovered. We call the weak communities the hidd...

Data sharing over the internet is increasingly efficient and increasingly anonymous. This growing anonymity makes it difficult for data users to exchange feedback: interesting ideas, innovations, ancillary data, concerns about data quality, etcetera. For data producers not knowing the end-users makes it more difficult to collect end-user feedback and tailor the production process to users needs...

Social networks are often organized into families, friendship groups, and institutional bodies. However, this organization, functional groups of friends and acquaintances especially, may not always be apparent from the characteristics of the individuals or from global measures of network structure. Community detection methods provide a means to identify cohesive and/or inter-dependent clusters ...

The web is rapidly becoming both more open and more social through the provision of technologies that make it easier for end users to access resources and join in social networks. Social networks have pioneered online communities, allowing users to contribute to collective knowledge by tagging online resources. Tagging behavior increased dramatically between 2005 and 2007. This paper reports on...

Revealing the latent community structure, which is crucial to understanding the features of networks, is an important problem in network and graph analysis. During the last decade, many approaches have been proposed to solve this challenging problem in diverse ways, i.e. different measures or data structures. Unfortunately, experimental reports on existing techniques fell short in validity and ...

Online social networks are a particular type of virtual community and in recent years they have attracted many people. These networks provide various features for users and allow them to construct a profile and share their information. Hence searching in users’ profiles and their information is one of the most common operations in social networks. In this paper, we present a search method based...

social life in the online world. It has become the most acceptable pattern of forging social connections on the web. Every new development has pros and cons in its own sphere, though, and social networking web sites are no different. Online social networks, being a part of the Web 2.0 world, are prone to attacks and malware infections. Social networks, such as Facebook, Twitter, MySpace, Orkut ...

The recent advancements in online social networks andmobile devices have provided valuable data sources to track users’ smartphone adoption, i.e., the usage of smartphones over time. An incisive understanding of users’ smartphone adoption can benefit many useful applications, ranging from user behavior understanding to targeted marketing. This paper studies smartphone adoption prediction in soc...

In this paper, a simple but efficient method of overlapping community detection is presented using local community gravitation in social networks. Given a highquality, non-overlapping partition generated by existing methods, this proposed method identifies the overlapping nodes from their surrounding partitioned communities according to their local community gravitation, with low computational ...