Abstract. Multidimensionality in social networks is a great issue that came out into view as a result of that most social media sites such as Facebook, Twitter, and YouTube allow people to interact with each other through different social activities. The community detection in such mul- tidimensional social networks has attracted a lot of attention in the resent years. When dealing with these networks the concept of community de- tection changes to be, the discovery of the shared group structure across all network dimensions such that members in the same group interact with each other more frequently than those outside the group. Most of the studies presented on the topic of community detection assume
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The web, telecommunication network, biological networks, and social networks are examples of systems mod- eled naturally as networks, where nodes in the network represent entities and
edges represent relationships between pairs of entities. The identification of com- munities hidden within the structure of these networks is an important task for a wide set of fields such as sociology, physics, and biology [1].
A network is said to have community structure if nodes of the network can be divided into groups such that nodes in the same group are frequently interact with each other more than those outside the group. Therefore, communities are groups of entities that probably share some common properties and/or play similar roles within the system that is being represented. Thus the ability to detect communities could be of significant practical importance. For examples; in a citation network communities may correspond to related papers on a single topic; in the web communities may correspond to web pages related to a single topic, which decrease the time needed by search engines to retrieve data by focusing on narrow but topically-related subsets of the web; also community can be considered as a summary of the whole network thus easy to …show more content…
These studies indi- cated that the more maximum the modularity the more robust the approach to detect communities, hence many optimization techniques applied to optimize the modularity as an objective such as greedy optimization[5] , extremal optimiza- tion [10] , simulated annealing [11], and genetic algorithms (GA) [12]. However, one aspect of such networks has been ignored so far: real networks are often multidimensional, i.e. two nodes may connect through many different types of connections, reflecting different types of relationships or interactions between entities.
Recently multidimensionality has been taken into account in a lot of works. In [13, 14] authors extended and defined a set of analytical measures which take into account the structure of multidimensional networks to open the way for new techniques to analyze and extract non trivial knowledge from this type of net- works. In [15–17] authors presented many integration strategies in order to map multidimensional network into mono-dimensional network to be able to
The web, telecommunication network, biological networks, and social networks are examples of systems mod- eled naturally as networks, where nodes in the network represent entities and
edges represent relationships between pairs of entities. The identification of com- munities hidden within the structure of these networks is an important task for a wide set of fields such as sociology, physics, and biology [1].
A network is said to have community structure if nodes of the network can be divided into groups such that nodes in the same group are frequently interact with each other more than those outside the group. Therefore, communities are groups of entities that probably share some common properties and/or play similar roles within the system that is being represented. Thus the ability to detect communities could be of significant practical importance. For examples; in a citation network communities may correspond to related papers on a single topic; in the web communities may correspond to web pages related to a single topic, which decrease the time needed by search engines to retrieve data by focusing on narrow but topically-related subsets of the web; also community can be considered as a summary of the whole network thus easy to …show more content…
These studies indi- cated that the more maximum the modularity the more robust the approach to detect communities, hence many optimization techniques applied to optimize the modularity as an objective such as greedy optimization[5] , extremal optimiza- tion [10] , simulated annealing [11], and genetic algorithms (GA) [12]. However, one aspect of such networks has been ignored so far: real networks are often multidimensional, i.e. two nodes may connect through many different types of connections, reflecting different types of relationships or interactions between entities.
Recently multidimensionality has been taken into account in a lot of works. In [13, 14] authors extended and defined a set of analytical measures which take into account the structure of multidimensional networks to open the way for new techniques to analyze and extract non trivial knowledge from this type of net- works. In [15–17] authors presented many integration strategies in order to map multidimensional network into mono-dimensional network to be able to