Zobrazeno 1 - 10
of 144
pro vyhledávání: '"Shin, Kijung"'
Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graph-structured data. To empower GNNs in heterophilic graphs, where adjacent nodes exhibit dissimilar labels or fea
Externí odkaz:
http://arxiv.org/abs/2405.20652
Exploring Edge Probability Graph Models Beyond Edge Independency: Concepts, Analyses, and Algorithms
Desirable random graph models (RGMs) should (i) be tractable so that we can compute and control graph statistics, and (ii) generate realistic structures such as high clustering (i.e., high subgraph densities). A popular category of RGMs (e.g., Erdos-
Externí odkaz:
http://arxiv.org/abs/2405.16726
Combinatorial optimization (CO) is naturally discrete, making machine learning based on differentiable optimization inapplicable. Karalias & Loukas (2020) adapted the probabilistic method to incorporate CO into differentiable optimization. Their work
Externí odkaz:
http://arxiv.org/abs/2405.08424
Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications, and thus investigation of deep learning for HOIs has become a valuable agenda for the data mining and machine learning communities. As networks of HOIs ar
Externí odkaz:
http://arxiv.org/abs/2404.01039
Hypergraphs are marked by complex topology, expressing higher-order interactions among multiple nodes with hyperedges, and better capturing the topology is essential for effective representation learning. Recent advances in generative self-supervised
Externí odkaz:
http://arxiv.org/abs/2404.00638
The success of a specific neural network architecture is closely tied to the dataset and task it tackles; there is no one-size-fits-all solution. Thus, considerable efforts have been made to quickly and accurately estimate the performances of neural
Externí odkaz:
http://arxiv.org/abs/2403.12821
To detect anomalies in real-world graphs, such as social, email, and financial networks, various approaches have been developed. While they typically assume static input graphs, most real-world graphs grow over time, naturally represented as edge str
Externí odkaz:
http://arxiv.org/abs/2402.11933
Recent studies have revealed that GNNs are vulnerable to adversarial attacks. To defend against such attacks, robust graph structure refinement (GSR) methods aim at minimizing the effect of adversarial edges based on node features, graph structure, o
Externí odkaz:
http://arxiv.org/abs/2402.11837
How would randomly shuffling feature vectors among nodes from the same class affect graph neural networks (GNNs)? The feature shuffle, intuitively, perturbs the dependence between graph topology and features (A-X dependence) for GNNs to learn from. S
Externí odkaz:
http://arxiv.org/abs/2402.04621
We address the medication recommendation problem, which aims to recommend effective medications for a patient's current visit by utilizing information (e.g., diagnoses and procedures) given at the patient's current and past visits. While there exist
Externí odkaz:
http://arxiv.org/abs/2312.12100