33,280 research outputs found
Two betweenness centrality measures based on Randomized Shortest Paths
This paper introduces two new closely related betweenness centrality measures
based on the Randomized Shortest Paths (RSP) framework, which fill a gap
between traditional network centrality measures based on shortest paths and
more recent methods considering random walks or current flows. The framework
defines Boltzmann probability distributions over paths of the network which
focus on the shortest paths, but also take into account longer paths depending
on an inverse temperature parameter. RSP's have previously proven to be useful
in defining distance measures on networks. In this work we study their utility
in quantifying the importance of the nodes of a network. The proposed RSP
betweenness centralities combine, in an optimal way, the ideas of using the
shortest and purely random paths for analysing the roles of network nodes,
avoiding issues involving these two paradigms. We present the derivations of
these measures and how they can be computed in an efficient way. In addition,
we show with real world examples the potential of the RSP betweenness
centralities in identifying interesting nodes of a network that more
traditional methods might fail to notice.Comment: Minor updates; published in Scientific Report
Term-Specific Eigenvector-Centrality in Multi-Relation Networks
Fuzzy matching and ranking are two information retrieval techniques widely used in web search. Their application to structured data, however, remains an open problem. This article investigates how eigenvector-centrality can be used for approximate matching in multi-relation graphs, that is, graphs where connections of many different types may exist. Based on an extension of the PageRank matrix, eigenvectors representing the distribution of a term after propagating term weights between related data items are computed. The result is an index which takes the document structure into account and can be used with standard document retrieval techniques. As the scheme takes the shape of an index transformation, all necessary calculations are performed during index tim
ChoiceRank: Identifying Preferences from Node Traffic in Networks
Understanding how users navigate in a network is of high interest in many
applications. We consider a setting where only aggregate node-level traffic is
observed and tackle the task of learning edge transition probabilities. We cast
it as a preference learning problem, and we study a model where choices follow
Luce's axiom. In this case, the marginal counts of node visits are a
sufficient statistic for the transition probabilities. We show how to
make the inference problem well-posed regardless of the network's structure,
and we present ChoiceRank, an iterative algorithm that scales to networks that
contains billions of nodes and edges. We apply the model to two clickstream
datasets and show that it successfully recovers the transition probabilities
using only the network structure and marginal (node-level) traffic data.
Finally, we also consider an application to mobility networks and apply the
model to one year of rides on New York City's bicycle-sharing system.Comment: Accepted at ICML 201
Ackermann Encoding, Bisimulations, and OBDDs
We propose an alternative way to represent graphs via OBDDs based on the
observation that a partition of the graph nodes allows sharing among the
employed OBDDs. In the second part of the paper we present a method to compute
at the same time the quotient w.r.t. the maximum bisimulation and the OBDD
representation of a given graph. The proposed computation is based on an
OBDD-rewriting of the notion of Ackermann encoding of hereditarily finite sets
into natural numbers.Comment: To appear on 'Theory and Practice of Logic Programming
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
How can we estimate the importance of nodes in a knowledge graph (KG)? A KG
is a multi-relational graph that has proven valuable for many tasks including
question answering and semantic search. In this paper, we present GENI, a
method for tackling the problem of estimating node importance in KGs, which
enables several downstream applications such as item recommendation and
resource allocation. While a number of approaches have been developed to
address this problem for general graphs, they do not fully utilize information
available in KGs, or lack flexibility needed to model complex relationship
between entities and their importance. To address these limitations, we explore
supervised machine learning algorithms. In particular, building upon recent
advancement of graph neural networks (GNNs), we develop GENI, a GNN-based
method designed to deal with distinctive challenges involved with predicting
node importance in KGs. Our method performs an aggregation of importance scores
instead of aggregating node embeddings via predicate-aware attention mechanism
and flexible centrality adjustment. In our evaluation of GENI and existing
methods on predicting node importance in real-world KGs with different
characteristics, GENI achieves 5-17% higher NDCG@100 than the state of the art.Comment: KDD 2019 Research Track. 11 pages. Changelog: Type 3 font removed,
and minor updates made in the Appendix (v2
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