108 research outputs found
GraLSP: Graph Neural Networks with Local Structural Patterns
It is not until recently that graph neural networks (GNNs) are adopted to
perform graph representation learning, among which, those based on the
aggregation of features within the neighborhood of a node achieved great
success. However, despite such achievements, GNNs illustrate defects in
identifying some common structural patterns which, unfortunately, play
significant roles in various network phenomena. In this paper, we propose
GraLSP, a GNN framework which explicitly incorporates local structural patterns
into the neighborhood aggregation through random anonymous walks. Specifically,
we capture local graph structures via random anonymous walks, powerful and
flexible tools that represent structural patterns. The walks are then fed into
the feature aggregation, where we design various mechanisms to address the
impact of structural features, including adaptive receptive radius, attention
and amplification. In addition, we design objectives that capture similarities
between structures and are optimized jointly with node proximity objectives.
With the adequate leverage of structural patterns, our model is able to
outperform competitive counterparts in various prediction tasks in multiple
datasets
Effective Semisupervised Community Detection Using Negative Information
The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints. In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community. Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection. The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities
Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets
Time series modeling has attracted extensive research efforts; however,
achieving both reliable efficiency and interpretability from a unified model
still remains a challenging problem. Among the literature, shapelets offer
interpretable and explanatory insights in the classification tasks, while most
existing works ignore the differing representative power at different time
slices, as well as (more importantly) the evolution pattern of shapelets. In
this paper, we propose to extract time-aware shapelets by designing a two-level
timing factor. Moreover, we define and construct the shapelet evolution graph,
which captures how shapelets evolve over time and can be incorporated into the
time series embeddings by graph embedding algorithms. To validate whether the
representations obtained in this way can be applied effectively in various
scenarios, we conduct experiments based on three public time series datasets,
and two real-world datasets from different domains. Experimental results
clearly show the improvements achieved by our approach compared with 17
state-of-the-art baselines.Comment: An extended version with 11 pages including appendix; Accepted by
AAAI'202
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