6,771 research outputs found
Dominating sets and ego-centered decompositions in social networks
Our aim here is to address the problem of decomposing a whole network into a
minimal number of ego-centered subnetworks. For this purpose, the network egos
are picked out as the members of a minimum dominating set of the network.
However, to find such an efficient dominating ego-centered construction, we
need to be able to detect all the minimum dominating sets and to compare all
the corresponding dominating ego-centered decompositions of the network. To
find all the minimum dominating sets of the network, we are developing a
computational heuristic, which is based on the partition of the set of nodes of
a graph into three subsets, the always dominant vertices, the possible dominant
vertices and the never dominant vertices, when the domination number of the
network is known. To compare the ensuing dominating ego-centered decompositions
of the network, we are introducing a number of structural measures that count
the number of nodes and links inside and across the ego-centered subnetworks.
Furthermore, we are applying the techniques of graph domination and
ego=centered decomposition for six empirical social networks.Comment: 17 pages, 7 figure
Academic team formation as evolving hypergraphs
This paper quantitatively explores the social and socio-semantic patterns of
constitution of academic collaboration teams. To this end, we broadly underline
two critical features of social networks of knowledge-based collaboration:
first, they essentially consist of group-level interactions which call for
team-centered approaches. Formally, this induces the use of hypergraphs and
n-adic interactions, rather than traditional dyadic frameworks of interaction
such as graphs, binding only pairs of agents. Second, we advocate the joint
consideration of structural and semantic features, as collaborations are
allegedly constrained by both of them. Considering these provisions, we propose
a framework which principally enables us to empirically test a series of
hypotheses related to academic team formation patterns. In particular, we
exhibit and characterize the influence of an implicit group structure driving
recurrent team formation processes. On the whole, innovative production does
not appear to be correlated with more original teams, while a polarization
appears between groups composed of experts only or non-experts only, altogether
corresponding to collectives with a high rate of repeated interactions
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%
Predicting links in ego-networks using temporal information
Link prediction appears as a central problem of network science, as it calls
for unfolding the mechanisms that govern the micro-dynamics of the network. In
this work, we are interested in ego-networks, that is the mere information of
interactions of a node to its neighbors, in the context of social
relationships. As the structural information is very poor, we rely on another
source of information to predict links among egos' neighbors: the timing of
interactions. We define several features to capture different kinds of temporal
information and apply machine learning methods to combine these various
features and improve the quality of the prediction. We demonstrate the
efficiency of this temporal approach on a cellphone interaction dataset,
pointing out features which prove themselves to perform well in this context,
in particular the temporal profile of interactions and elapsed time between
contacts.Comment: submitted to EPJ Data Scienc
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