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HARP: Hierarchical Representation Learning for Networks

作者: 学术君

时间: 2019-01-04 12:05

该论文提出了一种多层次的图表示学习的框架,可以提高原有的Graph Embedding方法(包括DeepWalk, LINE, Node2vec)的效果。框架首先从原 graph 开始一层层合并点(coarsen),再从最 coarse 的 graph 开始套用现有方法得到 embedding,并将其作为合并前的对应点的 initialization 进行新一轮的 embedding,直至返回原 graph。

论文名:
HARP: Hierarchical Representation Learning for Networks

会议/年份:AAAI 2018

作者:

Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena

推荐理由:

该论文提出了一种多层次的图表示学习的框架,可以提高原有的Graph Embedding方法(包括DeepWalk, LINE, Node2vec)的效果。框架首先从原 graph 开始一层层合并点(coarsen),再从最 coarse 的 graph 开始套用现有方法得到 embedding,并将其作为合并前的对应点的 initialization 进行新一轮的 embedding,直至返回原 graph。该框架在进行Graph Coarsening的时候,除了使用普通的Edge Collapsing来保持first-order proximity,也根据在真实数据上的观察提出了新的Star Collapsing来保持second-order proximity。最后,由于Graph Coarsening过程中图的节点数和边数呈指数级衰减,所以总体复杂度与原Graph Embedding方法相同。

Abstract


We present HARP, a novel method for learning low dimensional embeddings of a graph’s nodes which preserves higherorder structural features. Our proposed method achieves this by compressing the input graph prior to embedding it, effectively avoiding troublesome embedding configurations (i.e. local minima) which can pose problems to non-convex optimization. 

HARP works by finding a smaller graph which approximates the global structure of its input. This simplified graph is used to learn a set of initial representations, which serve as good initializations for learning representations in the original, detailed graph. We inductively extend this idea, by decomposing a graph in a series of levels, and then embed the hierarchy of graphs from the coarsest one to the original graph. 

HARP is a general meta-strategy to improve all of the stateof-the-art neural algorithms for embedding graphs, including DeepWalk, LINE, and Node2vec. Indeed, we demonstrate that applying HARP’s hierarchical paradigm yields improved implementations for all three of these methods, as evaluated on classification tasks on real-world graphs such as DBLP, BlogCatalog, and CiteSeer, where we achieve a performance gain over the original implementations by up to 14% Macro F1.

论文下载链接

https://www.aminer.cn/archive/harp-hierarchical-representation-learning-for-networks/599c7988601a182cd2648d44

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