12,916 research outputs found
Efficient computation of the Weighted Clustering Coefficient
The clustering coefficient of an unweighted network has been extensively used to quantify how tightly connected is the neighbor around a node and it has been widely adopted for assessing the quality of nodes in a social network. The computation of the clustering coefficient is challenging since it requires to count the number of triangles in the graph. Several recent works proposed efficient sampling, streaming and MapReduce algorithms that allow to overcome this computational bottleneck. As a matter of fact, the intensity of the interaction between nodes, that is usually represented with weights on the edges of the graph, is also an important measure of the statistical cohesiveness of a network. Recently various notions of weighted clustering coefficient have been proposed but all those techniques are hard to implement on large-scale graphs. In this work we show how standard sampling techniques can be used to obtain efficient estimators for the most commonly used measures of weighted clustering coefficient. Furthermore we also propose a novel graph-theoretic notion of clustering coefficient in weighted networks. © 2016, Copyright © Taylor & Francis Group, LL
Grouping complex systems: a weighted network comparative analysis
In this study, the authors compare two inter-municipal commuting networks (MCN) pertaining to the Italian islands of Sardinia and Sicily, by approaching their characterization through a weighted network analysis. They develop on
the results obtained for the MCN of Sardinia (De Montis et al. 2007) and attempt to use network analysis as a mean of detection of similarities or dissimilarities between the systems at hand
Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph
As the security landscape evolves over time, where thousands of species of
malicious codes are seen every day, antivirus vendors strive to detect and
classify malware families for efficient and effective responses against malware
campaigns. To enrich this effort, and by capitalizing on ideas from the social
network analysis domain, we build a tool that can help classify malware
families using features driven from the graph structure of their system calls.
To achieve that, we first construct a system call graph that consists of system
calls found in the execution of the individual malware families. To explore
distinguishing features of various malware species, we study social network
properties as applied to the call graph, including the degree distribution,
degree centrality, average distance, clustering coefficient, network density,
and component ratio. We utilize features driven from those properties to build
a classifier for malware families. Our experimental results show that
influence-based graph metrics such as the degree centrality are effective for
classifying malware, whereas the general structural metrics of malware are less
effective for classifying malware. Our experiments demonstrate that the
proposed system performs well in detecting and classifying malware families
within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201
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
Degree Ranking Using Local Information
Most real world dynamic networks are evolved very fast with time. It is not
feasible to collect the entire network at any given time to study its
characteristics. This creates the need to propose local algorithms to study
various properties of the network. In the present work, we estimate degree rank
of a node without having the entire network. The proposed methods are based on
the power law degree distribution characteristic or sampling techniques. The
proposed methods are simulated on synthetic networks, as well as on real world
social networks. The efficiency of the proposed methods is evaluated using
absolute and weighted error functions. Results show that the degree rank of a
node can be estimated with high accuracy using only samples of the
network size. The accuracy of the estimation decreases from high ranked to low
ranked nodes. We further extend the proposed methods for random networks and
validate their efficiency on synthetic random networks, that are generated
using Erd\H{o}s-R\'{e}nyi model. Results show that the proposed methods can be
efficiently used for random networks as well
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