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Found 10 papers, 4 papers with code
Hyperbolic Heterogeneous Graph Attention Networks
Most previous heterogeneous graph embedding models represent elements in a heterogeneous graph as vector representations in a low-dimensional Euclidean space.
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
To further efficiently exploit context information from metapath-based anomaly subgraph, we present a new framework, Metapath-based Graph Anomaly Detection (MGAD), incorporating GCN layers in both the dual-encoders and decoders to efficiently propagate context information between abnormal and normal nodes.
Graph Anomaly Detection
Semi-supervised Anomaly Detection
+1
A Unified Compression Framework for Efficient Speech-Driven Talking-Face Generation
Virtual humans have gained considerable attention in numerous industries, e. g., entertainment and e-commerce.
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges
Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems.
SiReN: Sign-Aware Recommendation Using Graph Neural Networks
In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy.
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Approximate Policy Iteration (API) algorithms alternate between (approximate) policy evaluation and (approximate) greedification.
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture
Federated learning has emerged as a new paradigm of collaborative machine learning; however, many prior studies have used global aggregation along a star topology without much consideration of the communication scalability or the diurnal property relied on clients' local time variety.
SSumM: Sparse Summarization of Massive Graphs
SSumM not only merges nodes together but also sparsifies the summary graph, and the two strategies are carefully balanced based on the minimum description length principle.
Databases Social and Information Networks H.2.8
Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends on the agent's uncertainty.
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement
We first provide a policy improvement result in an idealized setting, and then prove that our conditional CEM (CCEM) strategy tracks a CEM update per state, even with changing action-values.
