基于神经网络的PathSim在异构信息网络中的归纳相似性搜索
Neural PathSim for Inductive Similarity Search in Heterogeneous Information Networks
摘要：
PathSim是一种在异构信息网络中广泛使用的基于元路径的相似性模型。许多应用依赖于PathSim的计算，包括相似性搜索和聚类。在大型图上计算PathSim分数由于其高时间和存储复杂性而具有计算挑战性。在本文中，我们建议将近似真实PathSim分数的问题转化为学习问题。我们设计了一个基于编码器-解码器的框架NeuPath，其中考虑了PathSim的算法结构。具体地，编码器模块识别Top T个优化路径实例，其可以近似地面实况PathSim，并且将每个路径实例映射到嵌入向量。解码器将每个嵌入向量分别转换为标量，该标量标识相似性得分。我们在不同领域的两个真实世界的数据集上进行了广泛的实验，ACM和IMDB。我们的研究结果表明，NeuPath在PathSim近似任务和相似性搜索任务中的表现优于最先进的基线。
英文摘要：
PathSim is a widely used meta-path-based similarity in heterogeneous information networks. Numerous applications rely on the computation of PathSim, including similarity search and clustering. Computing PathSim scores on large graphs is computationally challenging due to its high time and storage complexity. In this paper, we propose to transform the problem of approximating the ground truth PathSim scores into a learning problem. We design an encoder-decoder based framework, NeuPath, where the algorithmic structure of PathSim is considered. Specifically, the encoder module identifies Top T optimized path instances, which can approximate the ground truth PathSim, and maps each path instance to an embedding vector. The decoder transforms each embedding vector into a scalar respectively, which identifies the similarity score. We perform extensive experiments on two real-world datasets in different domains, ACM and IMDB. Our results demonstrate that NeuPath performs better than state-of-the-art baselines in the PathSim approximation task and similarity search task.
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。
