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Characterizing Edges in Signed and Vector-Valued Graphs

Géraud Le Falher 1
1 MAGNET - Machine Learning in Information Networks
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189, Inria Lille - Nord Europe
Abstract : In this thesis, we develop methods to efficiently and accurately characterize edges in complex networks. In simple graphs, nodes are connected by a single semantic. For instance, two users are friends in a social networks, or there is a hypertext link from one webpage to another. Furthermore, those connections are typically driven by node similarity, in what is known as the homophily mechanism. In the previous examples, users become friends because of common features, and webpages link to each other based on common topics. By contrast, complex networks are graphs where every connection has one semantic among k possible ones. Those connections are moreover based on both partial homophily and heterophily of their endpoints. This additional information enable finer analysis of real world graphs. However, it can be expensive to acquire, or is sometimes not known beforehand. We address the problems of inferring edge semantics in various settings. First, we consider graphs where edges have two opposite semantics, and where we observe the label of some edges. These so-called signed graphs are a convenient way to represent polarized interactions. We propose two learning biases suited for directed and undirected signed graphs respectively. This leads us to design several algorithms leveraging the graph topology to solve a binary classification problem that we call Edge Sign Prediction. Second, we consider graphs with k ≥ 2 available semantics for edge. In that case of multilayer graphs, we are not provided with any edge label, but instead are given one feature vector for each node. Faced with such an unsupervised Edge Attributed Clustering problem, we devise a quality criterion expressing how well an edge k-partition and k semantical vectors explains the observed connections. We optimize this goodness of explanation criterion in vectorial and matricial forms, and show how those two methods perform on synthetic data.
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Submitted on : Tuesday, June 26, 2018 - 11:15:50 PM
Last modification on : Friday, December 11, 2020 - 6:44:07 PM
Long-term archiving on: : Wednesday, September 26, 2018 - 11:39:14 PM


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  • HAL Id : tel-01824215, version 1


Géraud Le Falher. Characterizing Edges in Signed and Vector-Valued Graphs. Artificial Intelligence [cs.AI]. Université de Lille, 2018. English. ⟨tel-01824215⟩



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