Network embedding has become the cornerstone of a variety of mining tasks, such as classification, link prediction, clustering, anomaly detection and many more, thanks to its superior ability to encode the intrinsic network characteristics in a compact lowdimensional space. Most of the existing methods focus on a single network and/or a single resolution, which generate embeddings of different network objects (node/subgraph/network) from different networks separately. A fundamental limitation with such methods is that the intrinsic relationship across different networks (e.g., two networks share same or similar subgraphs) and that across different resolutions (e.g., the nodesubgraph membership) are ignored, resulting in disparate embeddings. Consequentially, it leads to suboptimal performance or even becomes inapplicable for some downstream mining tasks (e.g., role classification, network alignment. etc.). In this paper, we propose a unified framework MrMine to learn the representations of objects from multiple networks at three complementary resolutions (i.e., network, subgraph and node) simultaneously. The key idea is to construct the crossresolution crossnetwork context for each object. The proposed method bears two distinctive features. First, it enables and/or boosts various multinetwork downstream mining tasks by having embeddings at different resolutions from different networks in the same embedding space. Second, Our method is efficientmore »
Fast and Accurate Network Embeddings via Very Sparse Random Projection
We present FastRP, a scalable and performant algorithm for learning distributed node representations in a graph. FastRP is over 4,000 times faster than stateoftheart methods such as DeepWalk and node2vec, while achieving comparable or even better performance as evaluated on several realworld networks on various downstream tasks. We observe that most network embedding methods consist of two components: construct a node similarity matrix and then apply dimension reduction techniques to this matrix. We show that the success of these methods should be attributed to the proper construction of this similarity matrix, rather than the dimension reduction method employed. FastRP is proposed as a scalable algorithm for network embeddings. Two key features of FastRP are: 1) it explicitly constructs a node similarity matrix that captures transitive relationships in a graph and normalizes matrix entries based on node degrees; 2) it utilizes very sparse random projection, which is a scalable optimizationfree method for dimension reduction. An extra benefit from combining these two design choices is that it allows the iterative computation of node embeddings so that the similarity matrix need not be explicitly constructed, which further speeds up FastRP. FastRP is also advantageous for its ease of implementation, parallelization and hyperparameter tuning. more »
 Award ID(s):
 1927227
 Publication Date:
 NSFPAR ID:
 10283034
 Journal Name:
 CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
 Page Range or eLocationID:
 399 to 408
 Sponsoring Org:
 National Science Foundation
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