93,877 research outputs found
An Introduction to Community Detection in Multi-layered Social Network
Social communities extraction and their dynamics are one of the most
important problems in today's social network analysis. During last few years,
many researchers have proposed their own methods for group discovery in social
networks. However, almost none of them have noticed that modern social networks
are much more complex than few years ago. Due to vast amount of different data
about various user activities available in IT systems, it is possible to
distinguish the new class of social networks called multi-layered social
network. For that reason, the new approach to community detection in the
multi-layered social network, which utilizes multi-layered edge clustering
coefficient is proposed in the paper.Comment: M.D. Lytras et al. (Eds.): WSKS 2011, CCIS 278, pp. 185-190, 201
Shortest Path Discovery in the Multi-layered Social Network
Multi-layered social networks consist of the fixed set of nodes linked by
multiple connections. These connections may be derived from different types of
user activities logged in the IT system. To calculate any structural measures
for multi-layered networks this multitude of relations should be coped with in
the parameterized way. Two separate algorithms for evaluation of shortest paths
in the multi-layered social network are proposed in the paper. The first one is
based on pre-processing - aggregation of multiple links into single
multi-layered edges, whereas in the second approach, many edges are processed
'on the fly' in the middle of path discovery. Experimental studies carried out
on the DBLP database converted into the multi-layered social network are
presented as well.Comment: This is an extended version of the paper ASONAM 2011, IEEE Computer
Society, pp. 497-501 DOI 10.1109/ASONAM.2011.6
Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks
Social networks existing among employees, customers or users of various IT
systems have become one of the research areas of growing importance. A social
network consists of nodes - social entities and edges linking pairs of nodes.
In regular, one-layered social networks, two nodes - i.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. Nowadays data about people
and their interactions, which exists in all 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 also gain understanding about semantic of human relations.
Are they direct or not? Do people tend to sustain single or multiple relations
with a given person? What types of communication is the most important for
them? Answers to these and more questions enable us to draw conclusions about
semantic of human interactions. Unfortunately, most of the methods used for
social network analysis (SNA) may be applied only to one-layered social
networks. Thus, some new structural measures for multi-layered social networks
are proposed in the paper, in particular: 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 for five different layers within the social network. 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.Comment: 16 pages, International Journal of Computational Intelligence System
A degree centrality in multi-layered social network
Multi-layered social networks reflect complex relationships existing in modern interconnected IT systems. In such a network each pair of nodes may be linked by many edges that correspond to different communication or collaboration user activities. Multi-layered degree centrality for multi-layered social networks is presented in the paper. Experimental studies were carried out on data collected from the real Web 2.0 site. The multi-layered social network extracted from this data consists of ten distinct layers and the network analysis was performed for different degree centralities measures
Multi-layered HITS on Multi-sourced Networks
abstract: Network mining has been attracting a lot of research attention because of the prevalence of networks. As the world is becoming increasingly connected and correlated, networks arising from inter-dependent application domains are often collected from different sources, forming the so-called multi-sourced networks. Examples of such multi-sourced networks include critical infrastructure networks, multi-platform social networks, cross-domain collaboration networks, and many more. Compared with single-sourced network, multi-sourced networks bear more complex structures and therefore could potentially contain more valuable information.
This thesis proposes a multi-layered HITS (Hyperlink-Induced Topic Search) algorithm to perform the ranking task on multi-sourced networks. Specifically, each node in the network receives an authority score and a hub score for evaluating the value of the node itself and the value of its outgoing links respectively. Based on a recent multi-layered network model, which allows more flexible dependency structure across different sources (i.e., layers), the proposed algorithm leverages both within-layer smoothness and cross-layer consistency. This essentially allows nodes from different layers to be ranked accordingly. The multi-layered HITS is formulated as a regularized optimization problem with non-negative constraint and solved by an iterative update process. Extensive experimental evaluations demonstrate the effectiveness and explainability of the proposed algorithm.Dissertation/ThesisMasters Thesis Computer Science 201
Layered Label Propagation: A MultiResolution Coordinate-Free Ordering for Compressing Social Networks
We continue the line of research on graph compression started with WebGraph,
but we move our focus to the compression of social networks in a proper sense
(e.g., LiveJournal): the approaches that have been used for a long time to
compress web graphs rely on a specific ordering of the nodes (lexicographical
URL ordering) whose extension to general social networks is not trivial. In
this paper, we propose a solution that mixes clusterings and orders, and devise
a new algorithm, called Layered Label Propagation, that builds on previous work
on scalable clustering and can be used to reorder very large graphs (billions
of nodes). Our implementation uses overdecomposition to perform aggressively on
multi-core architecture, making it possible to reorder graphs of more than 600
millions nodes in a few hours. Experiments performed on a wide array of web
graphs and social networks show that combining the order produced by the
proposed algorithm with the WebGraph compression framework provides a major
increase in compression with respect to all currently known techniques, both on
web graphs and on social networks. These improvements make it possible to
analyse in main memory significantly larger graphs
Extraction of Multi-layered Social Networks from Activity Data
The data gathered in all kind of web-based systems, which enable users to
interact with each other, provides an opportunity to extract social networks
that consist of people and relationships between them. The emerging structures
are very complex due to the number and type of discovered connections. In
webbased systems, the characteristic element of each interaction between users
is that there is always an object that serves as a communication medium. This
can be e.g. an email sent from one user to another or post at the forum
authored by one user and commented by others. Based on these objects and
activities that users perform towards them, different kinds of relationships
can be identified and extracted. Additional challenge arises from the fact that
hierarchies can exist between objects, e.g. a forum consists of one or more
groups of topics, and each of them contains topics that finally include posts.
In this paper, we propose a new method for creation of multi-layered social
network based on the data about users activities towards different types of
objects between which the hierarchy exists. Due to the flattening,
preprocessing procedure new layers and new relationships in the multi-layered
social network can be identified and analysed.Comment: 20 pages, 15 figure
Identifying critical nodes in multi-layered networks under multi-vector malware attack
This proceeding at: Net-Works 2013 International Conference. Complex and Multiplex Networks: Structure, Applications and Related Topic, took place 2013, February, 11-13, in El Escorial (Spain).Computer viruses are evolving by developing multiple spreading mechanisms that are simultaneously used during the infection process. The identification of the nodes that allow a better spreading efficiency of these kind of viruses is becoming a determinant part of the defensive strategy against malware. These multi-vector viruses can be modeled in multi-layered networks in which each node belongs simultaneously to different layers, adapting the spreading vector to the properties of the layer. This way, the same virus has different propagation rates in each layer and also in the multi-layered network considered as a whole. The set of nodes selected as initial group of infected subjects can determine the final propagation of the infection. In this work, we analyze the spreading of a virus in a multi-layered network formed by M layers, given different sets of initial infected nodes, and, in particular, the effect of the initial selection on the efficiency of the infection. The initial group of infected nodes is selected according to properties of the nodes considered as part of a layer and also of the whole system. As an example, we apply this study to a multi-layered network formed by two layers: the social network of collaboration of the Spanish scientific community of Statistical Physics, and the telecommunication network of each institution.We want to thank the financial support of MINECO through grants MTM2012-39101 for J.G. and FIS2011-22449 (PRODIEVO) for S.C., and of CM through grant S2009/ESP-1691 (MODELICO) for J.G. and S.C.Publicad
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