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Found 10 papers, 2 papers with code
Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks
Node importance estimation problem has been studied conventionally with homogeneous network topology analysis.
Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal
Visible watermarks, while instrumental in protecting image copyrights, frequently distort the underlying content, complicating tasks like scene interpretation and image editing.
Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space
We propose an end-to-end Bipartite Graph Convolutional Hashing approach, namely BGCH, which consists of three novel and effective modules: (1) adaptive graph convolutional hashing, (2) latent feature dispersion, and (3) Fourier serialized gradient estimation.
WISK: A Workload-aware Learned Index for Spatial Keyword Queries
In this paper, we propose WISK, a learned index for spatial keyword queries, which self-adapts for optimizing querying costs given a query workload.
GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs
Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path.
KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network
Keyword search is a fundamental task to retrieve information that is the most relevant to the query keywords.
Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model
With the surge of graph embedding mechanism, it has also been adopted to community detection.
Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View
In this paper, we model the propagation of the COVID-19 as spatio-temporal point processes and propose a generative and intensity-free model to track the spread of the disease.
RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating
In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation.
Inductive Link Prediction for Nodes Having Only Attribute Information
In attributed graphs, both the structure and attribute information can be utilized for link prediction.
