13 citations · 39 across the 10 of their papers we have counts for
10 papers
Positional Attention: Expressivity and Learnability of Algorithmic Computation
Artur Back de Luca, George Giapitzakis, Shenghao Yang +2
There is a growing interest in the ability of neural networks to execute algorithmic tasks (e.g., arithmetic, summary statistics, and sorting). The goal of this work is to better u…
Local Graph Clustering with Noisy Labels
Artur Back de Luca, Kimon Fountoulakis, Shenghao Yang
The growing interest in machine learning problems over graphs with additional node information such as texts, images, or labels has popularized methods that require the costly oper…
Polynomial Width is Sufficient for Set Representation with High-dimensional Features
Peihao Wang, Shenghao Yang, Shu Li +2
Set representation has become ubiquitous in deep learning for modeling the inductive bias of neural networks that are insensitive to the input order. DeepSets is the most widely us…
Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical Guarantees
Shenghao Yang, Kimon Fountoulakis
Local graph clustering methods aim to detect small clusters in very large graphs without the need to process the whole graph. They are fundamental and scalable tools for a wide ran…
On Classification Thresholds for Graph Attention with Edge Features
Kimon Fountoulakis, Dake He, Silvio Lattanzi +3
The recent years we have seen the rise of graph neural networks for prediction tasks on graphs. One of the dominant architectures is graph attention due to its ability to make pred…
Equivariant Hypergraph Diffusion Neural Operators
Peihao Wang, Shenghao Yang, Yunyu Liu +2
Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data and further solve relevant prediction…
Graph Attention Retrospective
Kimon Fountoulakis, Amit Levi, Shenghao Yang +2
Graph-based learning is a rapidly growing sub-field of machine learning with applications in social networks, citation networks, and bioinformatics. One of the most popular models…
Local Hyper-Flow Diffusion
Kimon Fountoulakis, Pan Li, Shenghao Yang
Recently, hypergraphs have attracted a lot of attention due to their ability to capture complex relations among entities. The insurgence of hypergraphs has resulted in data of incr…
Targeted Pandemic Containment Through Identifying Local Contact Network Bottlenecks
Shenghao Yang, Priyabrata Senapati, Di Wang +2
Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts--between individuals or between popula…
-Norm Flow Diffusion for Local Graph Clustering
Kimon Fountoulakis, Di Wang, Shenghao Yang
Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clu…