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Found 14 papers, 8 papers with code
On the Benefits of Attribute-Driven Graph Domain Adaptation
Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets.
One Node One Model: Featuring the Missing-Half for Graph Clustering
Most existing graph clustering methods primarily focus on exploiting topological structure, often neglecting the ``missing-half" node feature information, especially how these features can enhance clustering performance.
An Efficient Unsupervised Framework for Convex Quadratic Programs via Deep Unrolling
Unlike the standard learning-to-optimize framework that requires optimization solutions generated by solvers, our unsupervised method adjusts the network weights directly from the evaluation of the primal-dual gap.
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective
We conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking.
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
First, the absence of a comprehensive benchmark with unified problem settings hinders a clear understanding of the comparative effectiveness and practical value of different text-space GFMs.
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
In this work, we propose an FOM-unrolled neural network (NN) called PDHG-Net, and propose a two-stage L2O method to solve large-scale LP problems.
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models.
Simplified PCNet with Robustness
Graph Neural Networks (GNNs) have garnered significant attention for their success in learning the representation of homophilic or heterophilic graphs.
CDC: A Simple Framework for Complex Data Clustering
In particular, we deploy CDC to graph data of size 111M.
Provable Filter for Real-world Graph Clustering
Motivated by this finding, we construct two graphs that are highly homophilic and heterophilic, respectively.
PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering
This is attributed to their neglect of homophily in heterophilic graphs, and vice versa.
Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clustering
To overcome this drawback, we propose to learn a graph filter motivated by the theoretical analysis of Barlow Twins.
Revisiting Link Prediction: A Data Perspective
We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.
REQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range Effect
The reason for the range effect is that the predicted deviations both in a wide range and in a narrow range destroy the uniformity between MOS and pMOS.
