3,515 research outputs found
Self-organization of collaboration networks
We study collaboration networks in terms of evolving, self-organizing
bipartite graph models. We propose a model of a growing network, which combines
preferential edge attachment with the bipartite structure, generic for
collaboration networks. The model depends exclusively on basic properties of
the network, such as the total number of collaborators and acts of
collaboration, the mean size of collaborations, etc. The simplest model defined
within this framework already allows us to describe many of the main
topological characteristics (degree distribution, clustering coefficient, etc.)
of one-mode projections of several real collaboration networks, without
parameter fitting. We explain the observed dependence of the local clustering
on degree and the degree--degree correlations in terms of the ``aging'' of
collaborators and their physical impossibility to participate in an unlimited
number of collaborations.Comment: 10 pages, 8 figure
Contagions in Random Networks with Overlapping Communities
We consider a threshold epidemic model on a clustered random graph with
overlapping communities. In other words, our epidemic model is such that an
individual becomes infected as soon as the proportion of her infected neighbors
exceeds the threshold q of the epidemic. In our random graph model, each
individual can belong to several communities. The distributions for the
community sizes and the number of communities an individual belongs to are
arbitrary.
We consider the case where the epidemic starts from a single individual, and
we prove a phase transition (when the parameter q of the model varies) for the
appearance of a cascade, i.e. when the epidemic can be propagated to an
infinite part of the population. More precisely, we show that our epidemic is
entirely described by a multi-type (and alternating) branching process, and
then we apply Sevastyanov's theorem about the phase transition of multi-type
Galton-Watson branching processes. In addition, we compute the entries of the
matrix whose largest eigenvalue gives the phase transition.Comment: Minor modifications for the second version: added comments (end of
Section 3.2, beginning of Section 5.3); moved remark (end of Section 3.1,
beginning of Section 4.1); corrected typos; changed titl
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
In this paper, we propose GPSP, a novel Graph Partition and Space Projection
based approach, to learn the representation of a heterogeneous network that
consists of multiple types of nodes and links. Concretely, we first partition
the heterogeneous network into homogeneous and bipartite subnetworks. Then, the
projective relations hidden in bipartite subnetworks are extracted by learning
the projective embedding vectors. Finally, we concatenate the projective
vectors from bipartite subnetworks with the ones learned from homogeneous
subnetworks to form the final representation of the heterogeneous network.
Extensive experiments are conducted on a real-life dataset. The results
demonstrate that GPSP outperforms the state-of-the-art baselines in two key
network mining tasks: node classification and clustering.Comment: WWW 2018 Poste
N-body decomposition of bipartite networks
In this paper, we present a method to project co-authorship networks, that
accounts in detail for the geometrical structure of scientists collaborations.
By restricting the scope to 3-body interactions, we focus on the number of
triangles in the system, and show the importance of multi-scientists (more than
2) collaborations in the social network. This motivates the introduction of
generalized networks, where basic connections are not binary, but involve
arbitrary number of components. We focus on the 3-body case, and study
numerically the percolation transition.Comment: 5 pages, submitted to PR
Correlations in Bipartite Collaboration Networks
Collaboration networks are studied as an example of growing bipartite
networks. These have been previously observed to have structure such as
positive correlations between nearest-neighbour degrees. However, a detailed
understanding of the origin of this phenomenon and the growth dynamics is
lacking. Both of these are analyzed empirically and simulated using various
models. A new one is presented, incorporating empirically necessary ingredients
such as bipartiteness and sublinear preferential attachment. This, and a
recently proposed model of team assembly both agree roughly with some empirical
observations and fail in several others.Comment: 13 pages, 17 figures, 2 table, submitted to JSTAT; manuscript
reorganized, figures and a table adde
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