Link weights recovery in heterogeneous information networks

Autor: Robin Lamarche-Perrin, Hong-Lan Botterman
Přispěvatelé: ComplexNetworks, LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Complexes - Paris Ile-de-France (ISC-PIF), Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Sorbonne Université (SU)-École polytechnique (X)-École normale supérieure - Cachan (ENS Cachan)-Université Paris 1 Panthéon-Sorbonne (UP1)
Jazyk: angličtina
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
Physics - Physics and Society
Theoretical computer science
Semantics (computer science)
Computer science
Regression model
Heterogeneous information network
FOS: Physical sciences
02 engineering and technology
Physics and Society (physics.soc-ph)
Random walk
Link weight
[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
lcsh:QA75.5-76.95
020204 information systems
Node (computer science)
0202 electrical engineering
electronic engineering
information engineering
[INFO]Computer Science [cs]
Link (knot theory)
Structure (mathematical logic)
Social and Information Networks (cs.SI)
Sequence
lcsh:T58.5-58.64
lcsh:Information technology
Computer Science - Social and Information Networks
Computer Science Applications
Human-Computer Interaction
Modeling and Simulation
Probability distribution
020201 artificial intelligence & image processing
Enhanced Data Rates for GSM Evolution
lcsh:Electronic computers. Computer science
Information Systems
Zdroj: Computational Social Networks, Vol 8, Iss 1, Pp 1-26 (2021)
Computational Social Networks
Computational Social Networks, Springer, 2021, 8, pp.15. ⟨10.1186/s40649-020-00083-8⟩
ISSN: 2197-4314
DOI: 10.1186/s40649-020-00083-8⟩
Popis: Socio-technical systems usually consist of many intertwined networks, each connecting different types of objects or actors through a variety of means. As these networks are co-dependent, one can take advantage of this entangled structure to study interaction patterns in a particular network from the information provided by other related networks. A method is, hence, proposed and tested to recover the weights of missing or unobserved links in heterogeneous information networks (HIN)—abstract representations of systems composed of multiple types of entities and their relations. Given a pair of nodes in a HIN, this work aims at recovering the exact weight of the incident link to these two nodes, knowing some other links present in the HIN. To do so, probability distributions resulting from path-constrained random walks, i.e., random walks where the walker is forced to follow only a specific sequence of node types and edge types, capable to capture specific semantics and commonly called a meta-path, are combined in a linearly fashion to approximate the desired result. This method is general enough to compute the link weight between any types of nodes. Experiments on Twitter and bibliographic data show the applicability of the method.
Databáze: OpenAIRE
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