7 research outputs found
Centralities of Nodes and Influences of Layers in Large Multiplex Networks
(12 pages, 7 figures)(12 pages, 7 figures)(12 pages, 7 figures)(12 pages, 7 figures)(12 pages, 7 figures)We formulate and propose an algorithm (MultiRank) for the ranking of nodes and layers in large multiplex networks. MultiRank takes into account the full multiplex network structure of the data and exploits the dual nature of the network in terms of nodes and layers. The proposed centrality of the layers (influences) and the centrality of the nodes are determined by a coupled set of equations. The basic idea consists in assigning more centrality to nodes that receive links from highly influential layers and from already central nodes. The layers are more influential if highly central nodes are active in them. The algorithm applies to directed/undirected as well as to weighted/unweighted multiplex networks. We discuss the application of MultiRank to three major examples of multiplex network datasets: the European Air Transportation Multiplex Network, the Pierre Auger Multiplex Collaboration Network and the FAO Multiplex Trade Network
MultiGBS: A multi-layer graph approach to biomedical summarization
Automatic text summarization methods generate a shorter version of the input
text to assist the reader in gaining a quick yet informative gist. Existing
text summarization methods generally focus on a single aspect of text when
selecting sentences, causing the potential loss of essential information. In
this study, we propose a domain-specific method that models a document as a
multi-layer graph to enable multiple features of the text to be processed at
the same time. The features we used in this paper are word similarity, semantic
similarity, and co-reference similarity, which are modelled as three different
layers. The unsupervised method selects sentences from the multi-layer graph
based on the MultiRank algorithm and the number of concepts. The proposed
MultiGBS algorithm employs UMLS and extracts the concepts and relationships
using different tools such as SemRep, MetaMap, and OGER. Extensive evaluation
by ROUGE and BERTScore shows increased F-measure values
Quantifying the propagation of distress and mental disorders in social networks.
Heterogeneity of human beings leads to think and react differently to social phenomena. Awareness and homophily drive people to weigh interactions in social multiplex networks, influencing a potential contagion effect. To quantify the impact of heterogeneity on spreading dynamics, we propose a model of coevolution of social contagion and awareness, through the introduction of statistical estimators, in a weighted multiplex network. Multiplexity of networked individuals may trigger propagation enough to produce effects among vulnerable subjects experiencing distress, mental disorder, which represent some of the strongest predictors of suicidal behaviours. The exposure to suicide is emotionally harmful, since talking about it may give support or inadvertently promote it. To disclose the complex effect of the overlapping awareness on suicidal ideation spreading among disordered people, we also introduce a data-driven approach by integrating different types of data. Our modelling approach unveils the relationship between distress and mental disorders propagation and suicidal ideation spreading, shedding light on the role of awareness in a social network for suicide prevention. The proposed model is able to quantify the impact of overlapping awareness on suicidal ideation spreading and our findings demonstrate that it plays a dual role on contagion, either reinforcing or delaying the contagion outbreak
Multi-dimensional, multilayer, nonlinear and dynamic HITS
We introduce a ranking model for temporal multidimensional weighted and directed networks based on the Perron eigenvector of a multi-homogeneous order-preserving map. The model extends to the temporal multilayer setting the HITS algorithm and defines five centrality vectors: two for the nodes, two for the layers, and one for the temporal stamps. Nonlinearity is introduced in the standard HITS model in order to guarantee existence and uniqueness of these centrality vectors for any network, without any requirement on its connectivity structure. We introduce a globally convergent power iteration like algorithm for the computation of the centrality vectors. Numerical experiments on real-world networks are performed in order to assess the effectiveness of the proposed model and showcase the performance of the accompanying algorithm