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Dimmining: pruning-efficient and parallel graph mining on near-memory-computing
Graph mining, which finds specific patterns in the graph, is becoming increasingly important
in various domains. We point out that accelerating graph mining suffers from the following …
in various domains. We point out that accelerating graph mining suffers from the following …
Stmatch: accelerating graph pattern matching on gpu with stack-based loop optimizations
Graph pattern matching is a fundamental task in many graph analytics and graph mining
applications. As an NP-hard problem, it is often a performance bottleneck in these …
applications. As an NP-hard problem, it is often a performance bottleneck in these …
Contigra: graph mining with containment constraints
While graph mining systems employ efficient task-parallel strategies to quickly explore
subgraphs of interest (or matches), they remain oblivious to containment constraints like …
subgraphs of interest (or matches), they remain oblivious to containment constraints like …
Cyclosa:{Redundancy-Free} Graph Pattern Mining via Set Dataflow
Graph pattern mining is an essential task in many fields, which explores all the instances of
user-interested patterns in a data graph. Pattern-centric mining systems transform the …
user-interested patterns in a data graph. Pattern-centric mining systems transform the …
Large subgraph matching: a comprehensive and efficient approach for heterogeneous graphs
The subgraph matching problem is crucial in graph analysis, involving identifying all
instances of a given pattern P within a graph G. Advances in this field aim to uncover larger …
instances of a given pattern P within a graph G. Advances in this field aim to uncover larger …
Shogun: A task scheduling framework for graph mining accelerators
Graph mining is an emerging application of great importance to big data analytic. Graph
mining algorithms are bottle-necked by both computation complexity and memory access …
mining algorithms are bottle-necked by both computation complexity and memory access …
Arya: arbitrary graph pattern mining with decomposition-based sampling
Graph pattern mining is compute-intensive in processing massive amounts of graph-
structured data. This paper presents Arya, an ultra-fast approximate graph pattern miner that …
structured data. This paper presents Arya, an ultra-fast approximate graph pattern miner that …
A multi-source log semantic analysis-based attack investigation approach
Abstract As Advanced Persistent Threats (APT) become increasingly complex and
destructive, security analysts often use log data for performing attack investigation. Existing …
destructive, security analysts often use log data for performing attack investigation. Existing …
Accelerating graph mining systems with subgraph morphing
Graph mining applications analyze the structural properties of large graphs. These
applications are computationally expensive because finding structural patterns requires …
applications are computationally expensive because finding structural patterns requires …
Understanding High-Performance Subgraph Pattern Matching: A Systems Perspective
Subgraph isomorphism is a crucial problem in graph-analytics with wide-ranging
applications. This paper examines and compares two high-performance solutions to this …
applications. This paper examines and compares two high-performance solutions to this …