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Network embedding, which learns the low-dimensional representations of nodes, has gained significant research attention. Despite its superior empirical success, often measured by the prediction performance of downstream tasks (e.g., multi-label classification), it is unclear why a given embedding algorithm outputs the specific node representations, and how the resulting node representations relate to the structure of the input network. In this paper, we propose to discern the edge influence as the first step towards understanding skip-gram basd network embedding methods. For this purpose, we propose an auditing framework NEAR, whose key part includes two algorithms (NEAR-ADD and NEAR-DEL) to effectively and efficiently quantify the influence of each edge. Based on the algorithms, we further identify high-influential edges by exploiting the linkage between edge influence and the network structure. Experimental results demonstrate that the proposed algorithms (NEAR-ADD and NEAR-DEL) are significantly faster (up to 2, 000×) than straightforward methods with little quality loss. Moreover, the proposed framework can efficiently identify the most influential edges for network embedding in the context of downstream prediction task and adversarial attacking.
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Next waves in veridical network embedding*
Abstract Embedding nodes of a large network into a metric (e.g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences. Generally, a representation of a network object is learned in a Euclidean geometry and is then used for subsequent tasks regarding the nodes and/or edges of the network, such as community detection, node classification and link prediction. Network embedding algorithms have been proposed in multiple disciplines, often with domain‐specific notations and details. In addition, different measures and tools have been adopted to evaluate and compare the methods proposed under different settings, often dependent of the downstream tasks. As a result, it is challenging to study these algorithms in the literature systematically. Motivated by the recently proposed PCS framework for Veridical Data Science, we propose a framework for network embedding algorithms and discuss how the principles ofpredictability,computability, andstability(PCS) apply in this context. The utilization of this framework in network embedding holds the potential to motivate and point to new directions for future research.
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- Award ID(s):
- 1741191
- PAR ID:
- 10454395
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Statistical Analysis and Data Mining: The ASA Data Science Journal
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 1932-1864
- Page Range / eLocation ID:
- p. 5-17
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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