MAGE: Matching approximate patterns in richly-attributed graphs

Abstract

Given a large graph with millions of nodes and edges, say a social network where both its nodes and edges have multiple attributes (e.g., job titles, tie strengths), how to quickly find subgraphs of interest (e.g., a ring of businessmen with strong ties)? We present MAGE, a scalable, multicore subgraph matching approach that supports expressive queries over large, richly-attributed graphs. Our major contributions include: (1) MAGE supports graphs with both node and edge attributes (most existing approaches handle either one, but not both); (2) it supports expressive queries, allowing multiple attributes on an edge, wildcards as attribute values (i.e., match any permissible values), and attributes with continuous values; and (3) it is scalable, supporting graphs with several hundred million edges. We demonstrate MAGE's effectiveness and scalability via extensive experiments on large real and synthetic graphs, such as a Google+ social network with 460 million edges.

Scalable graph exploration and visualization: Sensemaking challenges and opportunities

“…Filtering [1], [2], [3] Sampling [4], [5], [6] Partitioning [7], [8], [9], [10] Clustering [11], [12], [13], [3], [14], [15] Local View Free Discovery Exploration [16], [17], [14], [18], [3], [15], [19], [20], [21] Network Motifs [22], [23], [24], [25], [26] Targeted Discovery Pattern Matching [27], [28], [29], [30], [31] Navigation [32], [33], [34], [35], [36], [19], [37] Fig. 1.…”

“…These include MAGE [28], Graphite [69], NeMa [27], TALE [29], and TopKDiv [30]; to name a few. This is essential in scenarios where the user already knows of an interesting pattern exactly or approximately, and wants to find where or how often it occurs in a larger graph.…”

Visage

“…These systems support the SPARQL querying language, which is hard to learn and use [10]. The Cypher language, designed for the recent Neo4j graph database 2 , is easier to work with since its syntax more closely resembles SQL [13, 15], but expressing relationships among nodes can still be challenging and may require writing many lines of code even for conceptually simple queries [14], as demonstrated in Figure 1, which seeks a “triangle” of three similar action films from the 1980’s [23]. …”

“…While there has been a lot of work in developing querying algorithms (e.g., [29, 28, 23]), there has been far less research on understanding and tackling the visualization, interaction, and usability challenges in the pattern specification process. Studying the user-facing aspects of subgraph matching is critical to fostering insights from interactive exploration and analysis.…”

Query complex graph patterns

“…[2,4]; (b) how to find a group of authors from databases, data mining and bioinformatics and they collaborate with each other in a starshape? [8,5]; (c) given a set of querying authors of interest, how to find somebody who initiates the research field these querying authors belong to, and how to summarize and visualize the querying authors? [3, 7, 1]; (d) how to incorporate users' preference into these complex queries [9, 10].…”