Discovering association rules from big graphs

Abstract

This paper tackles two challenges to discovery of graph rules. Existing discovery methods often (a) return an excessive number of rules, and (b) do not scale with large graphs given the intractability of the discovery problem. We propose an application-driven strategy to cut back rules and data that are irrelevant to users' interests, by training a machine learning (ML) model to identify data pertaining to a given application. Moreover, we introduce a sampling method to reduce a big graph G to a set H of small sample graphs. Given expected support and recall bounds, the method is able to deduce samples in H and mine rules from H to satisfy the bounds in the entire G . As proof of concept, we develop an algorithm to discover Graph Association Rules (GARs), which are a combination of graph patterns and attribute dependencies, and may embed ML classifiers as predicates. We show that the algorithm is parallelly scalable, i.e. , it guarantees to reduce runtime when more machines are used. We experimentally verify that the method is able to discover rules with recall above 91% when using sample ratio 10%, with speedup of 61 times.

An Efficient Approach for Discovering Graph Entity Dependencies (GEDs)

“…More related to this work, are techniques for rule discovery in property graphs. Examples of some notable works in this area include: 1) [17,27,55,56,57], which investigated the discovery of association rules in property graphs; and 2) [12,16,22,58] on mining keys and dependencies in property graphs-closest to this work. In particular, [12] presents a frequent sub-graph expansion based approach for mining keys in property graphs, whiles [16] proposes efficient parallel graph functional dependency discovery for large property graphs.…”

Anytime bottom-up rule learning for large-scale knowledge graph completion

“…3 if we would draw a sample of 50 groundings. In that case, we are not drawing a sample that is representative for the general rule (19), but instead we are drawing a sample for a more specific rule (20).…”

“…Several rule mining approaches applicable to large knowledge graphs have been proposed in the previous years [12,20,43,46]. Most of them have not been applied to knowledge graph completion tasks.…”

Supervised Knowledge Aggregation for Knowledge Graph Completion

“…It is shown to be competitive to neural approaches (Rossi et al, 2021;Meilicke et al, 2023) and it can be utilized to explain predictions made by embedding models (Betz et al, 2022a). Other approaches are tailored towards large graphs (Fan et al, 2022;Chen et al, 2016) or to learn negative rules (Ortona et al, 2018). There also exist attempts to improve rule quality by providing more advanced confidence computations (Galárraga et al, 2013;Pellissier Tanon et al, 2017;Zupanc & Davis, 2018).…”

“…Additionally, these approaches perform model theoretic entailment, which is too expensive in our settings, as KGs can consist of a large number of facts with millions of learned rules. Similarly, in the field of association rule mining, rule quality is often estimated for individual rules independently without considering the problem of aggregation (Galárraga et al, 2013;Chen et al, 2016;Ortona et al, 2018;Fan et al, 2022).…”