2024 Parallel Graph Learning with Temporal Stamp Encoding for Fraudulent Transactions Detections IEEE TBD 2024 Link Link 2024 Dispelling the Fake: Social Bot Detection Based on Edge Confidence Evaluation IEEE TNNLS 2024 Link Link 2024 Friend or Foe? Mining Suspicious Behavior via Graph Capsule Infomax ...
3d-reconstructionimage-matchingoutlier-rejectiongraph-neural-networkiccv2019 UpdatedApr 10, 2023 Python A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019). machine-learningdeep-learningclusteringword2veccommunity-detectionpytorchdeepwalkgensimfacto...
Hierarchical Graph Representation Learning with Local Capsule Pooling In this paper, a local capsule pooling network (LCPN) is proposed to alleviate the above issues. Specifically, (i) a local capsule pooling (LCP) is proposed to alleviate the issue of insufficient clustering; (ii) a task-aware...
We show t-SNE visualization of molecular representations to investigate the similarity of molecules with the same scaffold. Different colours represent different scaffolds, with a lower DB index indicating better clustering separation. b, Uniformity analysis. Molecular feature distributions are plotted with...
In this paper, we design a Component-aware Capsule Graph Network (CA-CGNet) to further address the features of embedding space based on the component constraints. Specifically, the dynamic clustering strategy is used to classify the features of patches produced by over-segmentation in...
In this paper, we use a graph capsule network to capture the spatial dependence of air quality data and meteorological data among cities, then use an LSTM network to model the temporal dependence of air pollution levels in specific cities and finally implement PM2.5 concentration prediction. We ...
(Fig.1c). This suggests that the integration of proper location information enhances spatial domain detection, with a larger graph constraint resulting in smoother and more contiguous clustering output (Additional file 1: Figs. S1–2). However, excessively largeλleads to the spatial constraints ...
Biomedical knowledge graph embedding with capsule network for multi-label drug–drug interaction prediction. IEEE Trans Knowl Data Eng. 2022. 20. Olayan RS, Ashoor H, Bajic VB. DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches....
Ren et al. [7] propose a graph convolutional network (GCN) that applies Wishart similarity to model the weighted graph edges in multiple scales, thus obtaining better performance. However, the graph nodes are the superpixels presegmented by spectral clustering, so pixel-wise PolSAR image ...
Li et al. introduce SSTD, a spatiotemporal demographic network for EEG-based emotion recognition that integrates spatial, temporal, and demographic information. SSTD utilizes adaptive time windowing and hierarchical clustering to handle individual differences. It employs GRU networks for time-domain ...