Dynamic Top-K Interesting Subgraph Query on Large-Scale Labeled Graphs

Abstract

A labeled graph is a special structure with node identification capability, which is often used in information networks, biological networks, and other fields. The subgraph query is widely used as an important means of graph data analysis. As the size of the labeled graph increases and changes dynamically, users tend to focus on the high-match results that are of interest to them, and they want to take advantage of the relationship and number of results to get the results of the query quickly. For this reason, we consider the individual needs of users and propose a dynamic Top-K interesting subgraph query. This method establishes a novel graph topology feature index (GTSF index) including a node topology feature index (NTF index) and an edge feature index (EF index), which can effectively prune and filter the invalid nodes and edges that do not meet the restricted condition. The multi-factor candidate set filtering strategy is proposed based on the GTSF index, which can be further pruned to obtain fewer candidate sets. Then, we propose a dynamic Top-K interesting subgraph query method based on the idea of the sliding window to realize the dynamic modification of the matching results of the subgraph in the dynamic evolution of the label graph, to ensure real-time and accurate results of the query. In addition, considering the factors, such as frequent Input/Output (I/O) and network communication overheads, the optimization mechanism of the graph changes and an incremental maintenance strategy for the index are proposed to reduce the huge cost of redundant operation and global updates. The experimental results show that the proposed method can effectively deal with a dynamic Top-K interesting subgraph query on a large-scale labeled graph, at the same time the optimization mechanism of graph changes and the incremental maintenance strategy of the index can effectively reduce the maintenance overheads.

Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

“…We use the interestingness score [14] to identify the quality of a matching subgraph, which is the sum of the weights of a matching subgraph in the data graph G. It is generally believed that the higher the interestingness score, the better the matching result. us, the personalized Top-K interesting subgraph query is used to search the top K matching subgraphs with the highest interestingness score under the condition that the query constraint is satisfied.…”

“…If the interestingness score of Size-c () is equal to or less than the bottom of the Top-K heap, then the result will be stored in the CM heap (lines 7-9). Else, the bottom of the Top-K heap will be replicated to the CM heap and deleted from the Top-K heap, and at the same time, the result will be stored in the Top-K heap (lines [11][12][13][14][15]. e time complexity of the matching algorithm is O (n), in which n is the number of edges in CES.…”

“…e number of edges for each synthetic graph is 10|V|. Labels of (12) for each μ i < INT(Top_i.top) do (13) for each cm in CM_i do (14) If INT(cm) < μ i (15) delete cm in CM_i; (16) Top-K_i, CM_i ← continue UBV (Size-c()) in i partition and INT > μ i ; (17) if Top-K_i NOT NULL do (18) CES ← Top-K_i; 12 Complexity nodes are randomly assigned from A ∼ E, and edge weights are randomly generated from [0,1], which is referenced in [13].…”

A Query-Driven System for Discovering Interesting Subgraphs in Social Media

“…On the "subjective" side of the interestingness problem, one approach considers interesting subgraphs as a subgraph matching problem (Shan et al, 2019). Their general idea is to compute all matching subgraphs that satisfy a user the query and then ranking the results based on the rarity and the likelihood of the associations among entities in the subgraphs.…”

Discovering Interesting Subgraphs in Social Media Networks

“…One of the earliest "graph mining" approaches focused on discovering the most frequently occurring subgraphs [3], [4]. A second approach considers interesting subgraphs as a subgraph matching problem [5]- [7]. Their general approach is to compute all matching subgraphs that satisfy a user the query and then ranking the results based on the rarity and the likelihood of the associations among entities in the subgraphs.…”