6 research outputs found
Fitness-driven deactivation in network evolution
Individual nodes in evolving real-world networks typically experience growth
and decay --- that is, the popularity and influence of individuals peaks and
then fades. In this paper, we study this phenomenon via an intrinsic nodal
fitness function and an intuitive aging mechanism. Each node of the network is
endowed with a fitness which represents its activity. All the nodes have two
discrete stages: active and inactive. The evolution of the network combines the
addition of new active nodes randomly connected to existing active ones and the
deactivation of old active nodes with possibility inversely proportional to
their fitnesses. We obtain a structured exponential network when the fitness
distribution of the individuals is homogeneous and a structured scale-free
network with heterogeneous fitness distributions. Furthermore, we recover two
universal scaling laws of the clustering coefficient for both cases, and , where and refer to the node degree and the
number of active individuals, respectively. These results offer a new simple
description of the growth and aging of networks where intrinsic features of
individual nodes drive their popularity, and hence degree.Comment: IoP Styl
Extending the definition of modularity to directed graphs with overlapping communities
Complex networks topologies present interesting and surprising properties,
such as community structures, which can be exploited to optimize communication,
to find new efficient and context-aware routing algorithms or simply to
understand the dynamics and meaning of relationships among nodes. Complex
networks are gaining more and more importance as a reference model and are a
powerful interpretation tool for many different kinds of natural, biological
and social networks, where directed relationships and contextual belonging of
nodes to many different communities is a matter of fact. This paper starts from
the definition of modularity function, given by M. Newman to evaluate the
goodness of network community decompositions, and extends it to the more
general case of directed graphs with overlapping community structures.
Interesting properties of the proposed extension are discussed, a method for
finding overlapping communities is proposed and results of its application to
benchmark case-studies are reported. We also propose a new dataset which could
be used as a reference benchmark for overlapping community structures
identification.Comment: 22 pages, 11 figure
Learning Dense Models of Query Similarity from User Click Logs
The goal of this work is to integrate query similarity metrics as features into a dense model that can be trained on large amounts of query log data, in order to rank query rewrites. We propose features that incorporate various notions of syntactic and semantic similarity in a generalized edit distance frame-work. We use the implicit feedback of user clicks on search results as weak labels in training linear ranking models on large data sets. We optimize different ranking objectives in a stochastic gradient descent framework. Our experiments show that a pairwise SVM ranker trained on multipartite rank levels outperforms other pairwise and listwise ranking methods under a variety of evaluation metrics