Attributed network embedding aims to learn lowdimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content. Because network topology structure and node attributes often exhibit high correlation, incorporating node attribute proximity into network embedding is beneficial for learning
good vector representations. In reality, large-scale networks often have incomplete/missing node content or linkages, yet existing attributed network embedding algorithms all operate under the assumption that networks are complete. Thus, their performance is vulnerable to missing data and suffers from poor scalability.
In this paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs. SINE formulates a probabilistic learning framework that separately models pairs of node-context and node-attribute relationships. Different from existing attributed
network embedding algorithms, SINE provides greater flexibility to make the best of useful information and mitigate negative effects of missing information on representation learning. A stochastic gradient descent based online algorithm is derived to learn node representations, allowing SINE to scale up to
large-scale networks with high learning efficiency. We evaluate the effectiveness and efficiency of SINE through extensive experiments on real-world networks. Experimental results confirm that SINE outperforms state-of-the-art baselines in various tasks, including node classification, node clustering, and link prediction, under settings with missing links and node attributes.
SINE is also shown to be scalable and efficient on large-scale networks with millions of nodes/edges and high-dimensional node features.
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Topical network embedding
Networked data involve complex information from multifaceted channels, including
topology structures, node content, and/or node labels etc., where structure and content
are often correlated but are not always consistent. A typical scenario is the citation
relationships in scholarly publications where a paper is cited by others not because
they have the same content, but because they share one or multiple subject matters.
To date, while many network embedding methods exist to take the node content into
consideration, they all consider node content as simple flat word/attribute set and
nodes sharing connections are assumed to have dependency with respect to all words
or attributes. In this paper, we argue that considering topic-level semantic interactions
between nodes is crucial to learn discriminative node embedding vectors. In order
to model pairwise topic relevance between linked text nodes, we propose topical
network embedding, where interactions between nodes are built on the shared latent
topics. Accordingly, we propose a unified optimization framework to simultaneously
learn topic and node representations from the network text contents and structures,
respectively. Meanwhile, the structure modeling takes the learned topic representations
as conditional context under the principle that two nodes can infer each other contingent
on the shared latent topics. Experiments on three real-world datasets demonstrate
that our approach can learn significantly better network representations, i.e., 4.1%
improvement over the state-of-the-art methods in terms of Micro-F1 on Cora dataset.
(The source code of the proposed method is available through the github link: https://
github.com/codeshareabc/TopicalNE.)
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- NSF-PAR ID:
- 10122921
- Date Published:
- Journal Name:
- Data Mining and Knowledge Discovery
- ISSN:
- 1384-5810
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10618-019-00659-7
