8,556 research outputs found
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
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 evolutionary behavior of the network nor to predict the
future status of individuals. Aside from being dynamic, another significant
characteristic of real-world social networks is the presence of leaders, i.e.
nodes with high degree centrality having a high attraction to absorb other
members and hence to form a local community. In this paper, we devised an
efficient method to incrementally detect communities in highly dynamic social
networks using the intuitive idea of importance and persistence of community
leaders over time. Our proposed method is able to find new communities based on
the previous structure of the network without recomputing them from scratch.
This unique feature, enables us to efficiently detect and track communities
over time rapidly. Experimental results on the synthetic and real-world social
networks demonstrate that our method is both effective and efficient in
discovering communities in dynamic social networks
Fast community structure local uncovering by independent vertex-centred process
This paper addresses the task of community detection and proposes a local
approach based on a distributed list building, where each vertex broadcasts
basic information that only depends on its degree and that of its neighbours. A
decentralised external process then unveils the community structure. The
relevance of the proposed method is experimentally shown on both artificial and
real data.Comment: 2015 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, Aug 2015, Paris, France. Proceedings of the 2015
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Minin
A Network Topology Approach to Bot Classification
Automated social agents, or bots, are increasingly becoming a problem on
social media platforms. There is a growing body of literature and multiple
tools to aid in the detection of such agents on online social networking
platforms. We propose that the social network topology of a user would be
sufficient to determine whether the user is a automated agent or a human. To
test this, we use a publicly available dataset containing users on Twitter
labelled as either automated social agent or human. Using an unsupervised
machine learning approach, we obtain a detection accuracy rate of 70%
Detecting highly overlapping community structure by greedy clique expansion
In complex networks it is common for each node to belong to several
communities, implying a highly overlapping community structure. Recent advances
in benchmarking indicate that existing community assignment algorithms that are
capable of detecting overlapping communities perform well only when the extent
of community overlap is kept to modest levels. To overcome this limitation, we
introduce a new community assignment algorithm called Greedy Clique Expansion
(GCE). The algorithm identifies distinct cliques as seeds and expands these
seeds by greedily optimizing a local fitness function. We perform extensive
benchmarks on synthetic data to demonstrate that GCE's good performance is
robust across diverse graph topologies. Significantly, GCE is the only
algorithm to perform well on these synthetic graphs, in which every node
belongs to multiple communities. Furthermore, when put to the task of
identifying functional modules in protein interaction data, and college dorm
assignments in Facebook friendship data, we find that GCE performs
competitively.Comment: 10 pages, 7 Figures. Implementation source and binaries available at
http://sites.google.com/site/greedycliqueexpansion
Egomunities, Exploring Socially Cohesive Person-based Communities
In the last few years, there has been a great interest in detecting
overlapping communities in complex networks, which is understood as dense
groups of nodes featuring a low outbound density. To date, most methods used to
compute such communities stem from the field of disjoint community detection by
either extending the concept of modularity to an overlapping context or by
attempting to decompose the whole set of nodes into several possibly
overlapping subsets. In this report we take an orthogonal approach by
introducing a metric, the cohesion, rooted in sociological considerations. The
cohesion quantifies the community-ness of one given set of nodes, based on the
notions of triangles - triplets of connected nodes - and weak ties, instead of
the classical view using only edge density. A set of nodes has a high cohesion
if it features a high density of triangles and intersects few triangles with
the rest of the network. As such, we introduce a numerical characterization of
communities: sets of nodes featuring a high cohesion. We then present a new
approach to the problem of overlapping communities by introducing the concept
of ego-munities, which are subjective communities centered around a given node,
specifically inside its neighborhood. We build upon the cohesion to construct a
heuristic algorithm which outputs a node's ego-munities by attempting to
maximize their cohesion. We illustrate the pertinence of our method with a
detailed description of one person's ego-munities among Facebook friends. We
finally conclude by describing promising applications of ego-munities such as
information inference and interest recommendations, and present a possible
extension to cohesion in the case of weighted networks
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