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Found 26 papers, 8 papers with code
Ultra-imbalanced classification guided by statistical information
Imbalanced data are frequently encountered in real-world classification tasks.
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks.
State Space Models on Temporal Graphs: A First-Principles Study
Recently, state space models (SSMs), which are framed as discretized representations of an underlying continuous-time linear dynamical system, have garnered substantial attention and achieved breakthrough advancements in independent sequence modeling.
Actor-Critic Reinforcement Learning with Phased Actor
We prove qualitative properties of PAAC for learning convergence of the value and policy, solution optimality, and stability of system dynamics.
On provable privacy vulnerabilities of graph representations
Our research primarily addresses the theoretical underpinnings of similarity-based edge reconstruction attacks (SERA), furnishing a non-asymptotic analysis of their reconstruction capacities.
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data.
Mitigating Estimation Errors by Twin TD-Regularized Actor and Critic for Deep Reinforcement Learning
We address the issue of estimation bias in deep reinforcement learning (DRL) by introducing solution mechanisms that include a new, twin TD-regularized actor-critic (TDR) method.
Privacy-preserving design of graph neural networks with applications to vertical federated learning
The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM).
Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs
Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily.
Self-supervision meets kernel graph neural models: From architecture to augmentations
Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks
Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy.
Oversmoothing: A Nightmare for Graph Contrastive Learning?
Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance.
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision representations, raising concerns about computation, memory footprint, and energy consumption burden (largely overlooked) for real-world applications.
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
We approach the problem by our proposed STEP, a self-supervised temporal pruning framework that learns to remove potentially redundant edges from input dynamic graphs.
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks.
DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk
Graph representation plays an important role in the field of financial risk control, where the relationship among users can be constructed in a graph manner.
GRANDE: a neural model over directed multigraphs with application to anti-money laundering
The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently.
Long N-step Surrogate Stage Reward to Reduce Variances of Deep Reinforcement Learning in Complex Problems
However, there is a lack of comprehensive and systematic study on this important aspect to demonstrate the effectiveness of multi-step methods in solving highly complex continuous control problems.
Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
We explore a new direction in that we can capture the evolving dynamics of temporal graphs with spiking neural networks (SNNs) instead of RNNs.
sqSGD: Locally Private and Communication Efficient Federated Learning
In addition, with fixed privacy and communication level, the performance of sqSGD significantly dominates that of various baseline algorithms.
What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders
The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding.
GUARD: Graph Universal Adversarial Defense
To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks.
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc.
Memory Augmented Design of Graph Neural Networks
Specifically, we allow every node in the original graph to interact with a group of memory nodes.
Practical Locally Private Federated Learning with Communication Efficiency
We also initialize a discussion about the role of quantization and perturbation in FL algorithm design with privacy and communication constraints.
Toward Reliable Designs of Data-Driven Reinforcement Learning Tracking Control for Euler-Lagrange Systems
Our proposed new design takes advantage of two control design frameworks: a reinforcement learning based, data-driven approach to provide the needed adaptation and (sub)optimality, and a backstepping based approach to provide closed-loop system stability framework.
