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Found 10 papers, 3 papers with code
LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection
However, a significant challenge in identifying fake news is the limited availability of labeled news datasets.
Heterophily-Aware Fair Recommendation using Graph Convolutional Networks
In this paper, we propose a fair GNN-based recommender system, called HetroFair, to improve items' side fairness.
Ranked #1 on
Recommendation Systems
on Amazon-Movies
Strong Transitivity Relations and Graph Neural Networks
Local neighborhoods play a crucial role in embedding generation in graph-based learning.
Ranked #1 on
Node Classification
on Citeseer
(1:1 Accuracy metric)
Content Augmented Graph Neural Networks
More precisely, we propose models wherein a structural embedding using a GNN and a content embedding are computed for each node.
The Embeddings World and Artificial General Intelligence
From early days, a key and controversial question inside the artificial intelligence community was whether Artificial General Intelligence (AGI) is achievable.
Effectively Counting s-t Simple Paths in Directed Graphs
An important tool in analyzing complex social and information networks is s-t simple path counting, which is known to be #P-complete.
Social and Information Networks Data Structures and Algorithms
Hierarchical Correlation Clustering and Tree Preserving Embedding
We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters applicable to both positive and negative pairwise dissimilarities.
Sublinear Update Time Randomized Algorithms for Dynamic Graph Regression
Existing exact algorithms for updating the solution of dynamic graph regression require at least a linear time (in terms of $n$: the size of the graph).
On the Theory of Dynamic Graph Regression Problem
Then, we show that given a n*m update-efficient matrix embedding (e. g., the adjacency matrix) and after an update operation in the graph, the exact optimal solution of linear regression can be updated in O(nm) time for the revised graph.
Learning Representations from Dendrograms
Then, to address the model selection problem, we study the aggregation of different dendrogram-based distances respectively in solution space and in representation space in the spirit of deep representations.
