Discovering Association Rules with Graph Patterns in Temporal Networks

Abstract

Discovering regularities between entities in temporal graphs is vital for many real-world applications (e.g., social recommendation, emergency event detection, and cyberattack event detection). This paper proposes temporal graph association rules (TGARs) that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and constraints. We introduce quality measures (e.g., support, confidence, and diversification) to characterize meaningful TGARs that are useful and diversified. In addition, the proposed support metric is an upper bound for alternative metrics, allowing us to guarantee a superset of patterns.We extend conventional confidence measures in terms of maximal occurrences of TGARs. The diversification score strikes a balance between interestingness and diversity. Although the problem is NP-hard, we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal graph. We propose pruning strategies to filter TGARs that have low support or cannot make top-k as early as possible. Moreover, we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search space. We experimentally verify the effectiveness, efficiency, and scalability of our algorithms in discovering diversified top-k TGARs from temporal graphs in real-life applications.

Geography of Technology Transfer in China

“…It is worth noting that temporal graph-based methods can hardly generate node representations directly using the adjacency matrix to aggregate neighbor information like a static graph-based method [8], [9]. Due to the special data form of the temporal graph that sorts node interactions by time, temporal methods are trained in batches of data [10]. Thus these methods typically store neighbors in interaction sequences and model future interactions from historical information.…”

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.…”

Security and Privacy in Metaverse: A Comprehensive Survey

“…The complete information of a user can build a user profile that reveals significant meta relations [73] . The user profiles, together with the knowledge graph, are typically used for recommendation systems [74,75] . However, massive users' privacy is at risk of being leaked from user profiling.…”

“…Sensory data leakage [61][62][63] , Biometrics leakage [68][69][70] Firewall [59] , Static scan [60] , End-to-end authentication protocol [64] , Two-factor [66] or three-factor [67] Authentication, local storage [72] Real world building Meta user relations [73][74][75] Graph-based framework for privacy preservation [77] , Differential privacy [78] Expandability Third-party tracking [81] , Cross-app tracking [79] Third-party tracking/cross-app tracking analysis tools and detection algorithms [83] , Machine learning based blocking model [84] Combination Virtual economy security [85] , Data security and privacy in digital twin [90] ,…”