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 low-dimensional 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 node-subgraph membership) are ignored, resulting in disparate embeddings. Consequentially, it leads to sub-optimal 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 cross-resolution cross-network context for each object. The proposed method bears two distinctive features. First, it enables and/or boosts various multi-network downstream mining tasks by having embeddings at different resolutions from different networks in the same embedding space. Second, Our method is efficient and scalable, with a O(nlog(n)) time complexity for the base algorithm and a linear time complexity w.r.t. the number of nodes and edges of input networks for the accelerated version. Extensive experiments on real-world data show that our methods (1) are able to enable and enhance a variety of multi-network mining tasks, and (2) scale up to million-node networks.
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ADMIRING: Adversarial Multi-network Mining
Multi-sourced networks naturally appear in many
application domains, ranging from bioinformatics, social networks,
neuroscience to management. Although state-of-the-art
offers rich models and algorithms to find various patterns when
input networks are given, it has largely remained nascent on how
vulnerable the mining results are due to the adversarial attacks.
In this paper, we address the problem of attacking multi-network
mining through the way of deliberately perturbing the networks
to alter the mining results. The key idea of the proposed method
(ADMIRING) is effective influence functions on the Sylvester
equation defined over the input networks, which plays a central
and unifying role in various multi-network mining tasks. The
proposed algorithms bear two main advantages, including (1)
effectiveness, being able to accurately quantify the rate of change
of the mining results in response to attacks; and (2) generality,
being applicable to a variety of multi-network mining tasks
( e.g., graph kernel, network alignment, cross-network node
similarity) with different attacking strategies (e.g., edge/node
removal, attribute alteration).
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- NSF-PAR ID:
- 10159293
- Date Published:
- Journal Name:
- ICDM
- Page Range / eLocation ID:
- 1522 to 1527
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDM.2019.00201
