A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Linear-time graph optimization using reinforcement learning.
DIFNET tackles the suspended animation problem in deep graph neural networks.
Graphs provide an efficient tool for object representation in various computer vision applications. Once graph-based representations are constructed, an important question is how to compare graphs. This problem is often formulated as a graph matching problem where one seeks a mapping between vertices of two graphs whic…
New GCNs solve graph embedding problems efficiently and interpretably.
Study on matching nodes between graphs to preserve edges, focusing on limits and algorithms.
Partial recovery of node mappings between correlated graphs is possible under specific conditions.
This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter…
Graph coloring involves assigning colors to the vertices of a graph such that two vertices linked by an edge receive different colors. Graph coloring problems are general models that are very useful to formulate many relevant applications and, however, are computationally difficult. In this work, a general population-b…
Solves Skopenkov's problem on graph embedding criteria.
New algorithm improves bandit with graph feedback by decomposing regret.
Graph matching is a challenging problem with very important applications in a wide range of fields, from image and video analysis to biological and biomedical problems. We propose a robust graph matching algorithm inspired in sparsity-related techniques. We cast the problem, resembling group or collaborative sparsity f…
Survey of graph learning methods for combinatorial optimization problems.
Graphs are naturally sparse objects that are used to study many problems involving networks, for example, distributed learning and graph signal processing. In some cases, the graph is not given, but must be learned from the problem and available data. Often it is desirable to learn sparse graphs. However, making a grap…
The paper solves graph realization problems for Reeb graphs of Morse functions.
Graphs are fundamental mathematical structures used in various fields to represent data, signals and processes. In this paper, we propose a novel framework for learning/estimating graphs from data. The proposed framework includes (i) formulation of various graph learning problems, (ii) their probabilistic interpretatio…
Detects graph topology changes from noisy signals using prior spectral information.
Proposes GIB for recognizing informative subgraphs in graphs.
Two CSSL-based methods improve graph classification with limited labeled data.
Paper converts graph learning to lifelong learning.
Unified framework for graph coarsening using node features and graph matrices.
Poisson learning doesn't solve graph semi-supervised learning issues.
DeepMap learns deep graph representations via CNNs, improving graph classification performance.
We present a novel framework based on optimal transport for the challenging problem of comparing graphs. Specifically, we exploit the probabilistic distribution of smooth graph signals defined with respect to the graph topology. This allows us to derive an explicit expression of the Wasserstein distance between graph s…
The paper proves index theorems for graph-based optimal control problems.
GRAPH-BERT uses only attention for graph representation learning.
A regularized optimization problem over a large unstructured graph is studied, where the regularization term is tied to the graph geometry. Typical regularization examples include the total variation and the Laplacian regularizations over the graph. When applying the proximal gradient algorithm to solve this problem, t…
Graph Neural Networks solve topology problems in simple 3D models.
Graph neural Thompson Sampling improves online decision-making for graph data.
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…
Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs. The seeded graph matching problem is the graph matching problem when we are first given a partial alignment that we are tasked with completing. In this paper, we m…
Surveying machine learning for solving graph optimization problems.
This work improves graph inference using the degree-4 sum-of-squares hierarchy.
In this paper we discuss four problems regarding Markov equivalences for subclasses of loopless mixed graphs. We classify these four problems as finding conditions for internal Markov equivalence, which is Markov equivalence within a subclass, for external Markov equivalence, which is Markov equivalence between subclas…
Recent papers have formulated the problem of learning graphs from data as an inverse covariance estimation with graph Laplacian constraints. While such problems are convex, existing methods cannot guarantee that solutions will have specific graph topology properties (e.g., being -partite), which are desirable for so…
Researchers prove it's impossible to partially recover graph alignments in certain conditions.
We consider the problem of learning a causal graph over a set of variables with interventions. We study the cost-optimal causal graph learning problem: For a given skeleton (undirected version of the causal graph), design the set of interventions with minimum total cost, that can uniquely identify any causal graph with…
NeuroMatch efficiently matches subgraphs in large graphs using neural networks.
This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks (GNN), which have emerged as an effective model for various supervised prediction problems defined on structured data, can be trained to pr…
Detecting edge correlation between two graphs sharpens a threshold based on densest subgraph.
New methods cluster and test graphs without vertex correspondence.
Kernel and linear regression have been recently explored in the prediction of graph signals as the output, given arbitrary input signals that are agnostic to the graph. In many real-world problems, the graph expands over time as new nodes get introduced. Keeping this premise in mind, we propose a method to recursively …
Bandit problem on graphs aims to recommend items with high expected ratings.
Study bandit problem on smooth graph functions for recommender systems.
Paper learns Cartesian product graphs with Laplacian constraints.
In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously. We first cast the graph structure learning problem as a similarity metric learning problem and…
The problem of finding the vertex correspondence between two noisy graphs with different number of vertices where the smaller graph is still large has many applications in social networks, neuroscience, and computer vision. We propose a solution to this problem via a graph matching matched filter: centering and padding…
Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. If this is not possible, the data structure has to be inferred f…
Graph-Coupled Oscillator Networks (GraphCON) tackles graph-based learning problems.