Abstract
Tie strength allows to classify social relationships and identify different types of them. For instance, social relationships can be classified as persistent and similar based respectively on the regularity with which they occur and the similarity among them. On the other hand, rare and somewhat similar relationships are random and cause noise in a social network, thus hiding the actual structure of the network and preventing an accurate analysis of it. In this article, we propose a method to handle social network data that exploits temporal features to improve the detection of communities by existing algorithms. By removing random relationships, we observe that social networks converge to a topology with more pure social relationships and better quality community structures.
Highlights
Social networks may change their structure due to interactions among their members over time
Note that we show the results for the original social networks and for the filtered ones, i.e., those generated after the complete removal of random relationships
Vertices that have all their relations classified as random are disconnected from the network because they do not correspond to members of any community
Summary
Social networks may change their structure due to interactions among their members over time. Most networks are not really static [1, 3], which means that not considering temporal dimension may cause loss of information with respect to the order and proximity of the interactions, i.e., the evolution pattern of the community structure is lost [2]. This simplification causes informational noise in the social relations, which can lead to errors in the individuals’ membership in their respective communities. Consider a group of people who do not know
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