Collective Knowledge Composition in a P2P Network

Aleman-Meza, Boanerges; Halaschek-Wiener, Christian; Arpinar, I. Budak

doi:10.4018/9781591405603.ch013

Cited by 1 publication

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“…Such works are usually to some extend based on the existing methods for single-layered networks. The investigated topics are among others: community mining 24,25 , ranking network's nodes 26 and paths 27 , shortest 28 and unique 29 paths finding. The researchers in the field of multi-layered networks also try to develop new models of networks that capture the multi-relational characteristics of data.…”

Section: Published By Atlantis Pressmentioning

confidence: 99%

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Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks

Bródka¹,

Kazienko²,

Musiał³

et al. 2012

IJCIS

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Social networks existing among employees, customers or other types of users of various IT systems have become one of the research areas of growing importance. Data about people and their interactions that exist in social media, provides information about many different types of relationships within one network. Analysing this data one can obtain knowledge not only about the structure and characteristics of the network but it also enables to understand the semantic of human relations. Each social network consists of nodes -social entities and edges linking pairs of nodes. In regular, one-layered networks, two nodesi.e. people are connected with a single edge whereas in the multi-layered social networks, there may be many links of different types for a pair of nodes. Most of the methods used for social network analysis (SNA) may be applied only to one-layered networks. Thus, some new structural measures for multi-layered social networks are proposed in the paper. This study focuses on definitions and analysis of cross-layer clustering coefficient, cross-layer degree centrality and various versions of multi-layered degree centralities. Authors also investigated the dynamics of multi-layered neighbourhood. The evaluation of the presented concepts on the real-world dataset is presented. The measures proposed in the paper may directly be used to various methods for collective classification, in which nodes are assigned to labels according to their structural input features.

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