基于群体行为的快速子图同构算法
Stigmergic Swarming Agents for Fast Subgraph Isomorphism
AI总结：
ASSIST是一种基于群体行为的快速子图同构算法，通过线性时间复杂度解决NP难问题，支持复杂匹配需求。
摘要：
最大部分子图同构性比较两个图（由边连接的节点）以找到最大的公共子图。在带有标签节点的图中，一个常见用例是寻找查询图中的q个节点在数据图中的实例。该问题属于NP难问题，朴素解法的复杂度为q + d的指数级。目前最快的启发式方法复杂度为O(d²)。本文提出了ASSIST（通过群体行为的近似子图同构算法），受旅行商问题的蚁群优化方法启发。在O(q·log(d))时间内完成节点匹配后，ASSIST的迭代子图搜索时间与查询规模线性相关，与数据规模无关。ASSIST可以扩展以支持匹配问题（如时间有序边、不精确匹配、数据图中缺失的节点或边），这些问题是其他启发式方法难以处理的。
英文摘要：
Maximum partial subgraph isomorphism compares two graphs (nodes joined by edges) to find a largest common subgraph. A common use case, for graphs with labeled nodes, seeks to find instances of a \textit{query} graph with $q$ nodes in a (typically larger) \textit{data} graph with $d$ nodes. The problem is NP-complete, and naïve solutions are exponential in $q + d$. The fastest current heuristic has complexity $O(d^2)$. This paper outlines ASSIST (Approximate Swarming Subgraph Isomorphism through Stigmergy), inspired by the ant colony optimization approach to the traveling salesperson. After peering (identifying matching individual nodes in query and data) in time $O(q\cdot log(d))$, the time required for ASSIST's iterative subgraph search, the combinatorially complex part of the problem, is linear in query size and constant in data size. ASSIST can be extended to support matching problems (such as temporally ordered edges, inexact matches, and missing nodes or edges in the data graph) that frustrate other heuristics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
