Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single network independently and imposes relevance regularization on the network learning at different time steps. Such a snapshot scheme fails to leverage topology similarity between temporal networks for progressive training. In addition to the static node relationships within each network, nodes could show similar variation patterns (e.g., change of local structures) within the temporal network sequence. Both static node structures and temporal variation patterns can be combined to better characterize node affinities for unified embedding learning. In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. Instead of training graph attention weights for each network independently, we allow model weights to share and evolve across all temporal networks based on their respective topology discrepancies. Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks.
Node, Motif and Subgraph: Leveraging Network Functional Blocks Through Structural Convolution
Networks or graphs provide a natural and generic
way for modeling rich structured data. Recent research on graph
analysis has been focused on representation learning, of which
the goal is to encode the network structures into distributed
embedding vectors, so as to enable various downstream applications
through off-the-shelf machine learning. However, existing
methods mostly focus on node-level embedding, which is insufficient
for subgraph analysis. Moreover, their leverage of network
structures through path sampling or neighborhood preserving is
implicit and coarse. Network motifs allow graph analysis in a
finer granularity, but existing methods based on motif matching
are limited to enumerated simple motifs and do not leverage
node labels and supervision. In this paper, we develop NEST, a
novel hierarchical network embedding method combining motif
filtering and convolutional neural networks. Motif-based filtering
enables NEST to capture exact small structures within networks,
and convolution over the filtered embedding allows it to fully
explore complex substructures and their combinations. NEST
can be trivially applied to any domain and provide insight
into particular network functional blocks. Extensive experiments
on protein function prediction, drug toxicity prediction and
social network community identification have demonstrated its
effectiveness and efficiency.
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- PAR ID:
- 10079176
- Date Published:
- Journal Name:
- IEEE/ACM 2018 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018
- Volume:
- 2018
- Issue:
- 1
- Page Range / eLocation ID:
- 47 to 52
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Network representation learning (NRL) is crucial in the area of graph learning. Recently, graph autoencoders and its variants have gained much attention and popularity among various types of node embedding approaches. Most existing graph autoencoder-based methods aim to minimize the reconstruction errors of the input network while not explicitly considering the semantic relatedness between nodes. In this paper, we propose a novel network embedding method which models the consistency across different views of networks. More specifically, we create a second view from the input network which captures the relation between nodes based on node content and enforce the latent representations from the two views to be consistent by incorporating a multiview adversarial regularization module. The experimental studies on benchmark datasets prove the effectiveness of this method, and demonstrate that our method compares favorably with the state-of-the-art algorithms on challenging tasks such as link prediction and node clustering. We also evaluate our method on a real-world application, i.e., 30-day unplanned ICU readmission prediction, and achieve promising results compared with several baseline methods.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ASONAM.2018.8508729
