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Knowledge Representation Analysis of Graph Mining KU Leuven
This paper analyses the graph mining problem, and the frequent pattern mining task associated with it. In general, frequent pattern mining looks for a graph which occurs frequently within a network or, in the transactional setting, within a dataset of graphs. We discuss this task in the transactional setting, which is a problem of interest in many fields such as bioinformatics, chemoinformatics and social networks. We look at the graph mining ...
Graph mining KU Leuven
Graph mining is the study of how to perform data mining and machine learning on data represented with graphs. One can distinguish between, on the one hand, transactional graph mining, where a database of separate, independent graphs is considered (such as databases of molecules and databases of images), and, on the other hand, large network analysis, where a single large network is considered (such as chemical interaction networks and concept ...
Subgraph Mining for Graph Neural Networks KU Leuven
While Graph Neural Networks (GNNs) are state-of-the-art models for graph learning, they are only as expressive as the first-order Weisfeiler-Leman graph isomorphism test algorithm. To enhance their expressiveness one can incorporate complex structural information as attributes of the nodes in input graphs. However, this typically demands significant human effort and specialised domain knowledge. We demonstrate the feasibility of automatically ...
Graph and network pattern mining KU Leuven
In this chapter, we survey graph mining methods. We focus on graph pattern mining, but also discuss a number of related topics such as generative models and the patterns emerging from them.
Mining graph evolution rules KU Leuven
In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern that describe the evolution of large networks over time, at a local level. Given a sequence of snapshots of an evolving graph, we aim at discovering rules describing the local changes occurring in it. Adopting a definition of support based on minimum image we study the problem of extracting patterns whose frequency is larger than a minimum support ...
Mining the Enriched Subgraphs for Specific Vertices in a Biological Graph Flanders Institute for Biotechnology Ghent University University of Antwerp
Knowledge representation analysis of graph mining KU Leuven
Many problems, especially those with a composite structure, can nat-urally be expressed in higher order logic. From a KR perspective modeling these problems in an intuitive way is a challenging task. In this paper we study the graph mining problem as an example of a higher order problem. In short, this problem asks us to find a graph that frequently occurs as a subgraph among a set of example graphs. We start from the problem’s mathematical ...
Mining closed patterns in relational, graph and network data KU Leuven
Recent theoretical insights have led to the introduction of efficient algorithms for mining closed item-sets. This paper investigates potential generalizations of this paradigm to mine closed patterns in relational, graph and network databases. Several semantics and associated definitions for closed patterns in relational data have been introduced in previous work, but the differences among these and the implications of the choice of semantics ...