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.
Graph Posterior Network improves uncertainty estimation for node classification in interdependent graphs.
Proposes active learning for meta-learning in graph node response prediction.
This paper tackles graph translation challenges by predicting both node and edge attributes simultaneously.
We consider the two problems of predicting links in a dynamic graph sequence and predicting functions defined at each node of the graph. In many applications, the solution of one problem is useful for solving the other. Indeed, if these functions reflect node features, then they are related through the graph structure.…
SPARC tackles cold-start nodes in graphs by using spectral embeddings.
GEN tackles few-shot out-of-graph link prediction in evolving multi-relational graphs.
Learning node embeddings that capture a node's position within the broader graph structure is crucial for many prediction tasks on graphs. However, existing Graph Neural Network (GNN) architectures have limited power in capturing the position/location of a given node with respect to all other nodes of the graph. Here w…
Graph attention networks improve performance on heterogeneous graphs.
PanRep learns universal node embeddings for heterogeneous graphs.
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 …
This work investigates how GCNs should handle local structure discrepancies in testing nodes.
We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link pr…
Graph auto-encoder predicts unobserved node features from biological networks and omics data.
Recently, graph neural networks (GNNs) have proved to be suitable in tasks on unstructured data. Particularly in tasks as community detection, node classification, and link prediction. However, most GNN models still operate with static relationships. We propose the Graph Learning Network (GLN), a simple yet effective p…
In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different graph prediction tasks. We present a joint loss function that cre…
DEAL model predicts links for new nodes with only attribute info.
Improves GNN prediction sets with conformal prediction for node classification.
GG-SAGE predicts links in directed graphs with attributes, outperforming existing methods.
Proposes a method to quantify uncertainty in graph neural networks for node classification.
CopulaGNN integrates graph representational and correlational roles for better node-level predictions.
DE improves GNNs by distinguishing graph substructures, enhancing accuracy.
Graph InfoClust learns node representations by capturing cluster-level information, improving graph mining tasks.
Framework learns dynamic graph attributes and links co-evolution.
Embedding graph nodes into a vector space can allow the use of machine learning to e.g. predict node classes, but the study of node embedding algorithms is immature compared to the natural language processing field because of a diverse nature of graphs. We examine the performance of node embedding algorithms with respe…
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges…
Persona2vec learns multiple node roles in graphs.
We develop the Latent Multi-group Membership Graph (LMMG) model, a model of networks with rich node feature structure. In the LMMG model, each node belongs to multiple groups and each latent group models the occurrence of links as well as the node feature structure. The LMMG can be used to summarize the network structu…
RoCP-GNN improves GNNs' robustness in graph node classification.
New method detects novel node categories in graphs with distribution shifts.
TuneUp improves GNN training by focusing on hard-to-learn nodes.
Graph transformers outperform graph convolutions by preserving community information.
To take full advantage of fast-growing unlabeled networked data, this paper introduces a novel self-supervised strategy for graph representation learning by exploiting natural supervision provided by the data itself. Inspired by human social behavior, we assume that the global context of each node is composed of all no…
Proposes a novel approach using vector cross product to preserve directional edges in directed graphs.
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization. Previous methods on graph representation learning mainly focus on static graphs, however, many real-world graphs are dynamic and evol…
CatGCN improves GCNs by modeling feature interactions for categorical node features.
The study analyzes and benchmarks graph conformal prediction methods.
The accurate and interpretable prediction of future events in time-series data often requires the capturing of representative patterns (or referred to as states) underpinning the observed data. To this end, most existing studies focus on the representation and recognition of states, but ignore the changing transitional…
RR-GNN improves GNN prediction intervals by accounting for graph heteroscedasticity and structural biases.
We investigate the problem of sequentially predicting the binary labels on the nodes of an arbitrary weighted graph. We show that, under a suitable parametrization of the problem, the optimal number of prediction mistakes can be characterized (up to logarithmic factors) by the cutsize of a random spanning tree of the g…
GOAT learns multiple node representations from graph structure alone.
We propose a novel approach for learning node representations in directed graphs, which maintains separate views or embedding spaces for the two distinct node roles induced by the directionality of the edges. We argue that the previous approaches either fail to encode the edge directionality or their encodings cannot b…
Graph embedding has recently gained momentum in the research community, in particular after the introduction of random walk and neural network based approaches. However, most of the embedding approaches focus on representing the local neighborhood of nodes and fail to capture the global graph structure, i.e. to retain …
Study on predicting graph labels at nodes using local averaging and distance estimation.
In this work, we study semi-supervised multi-label node classification problem in attributed graphs. Classic solutions to multi-label node classification follow two steps, first learn node embedding and then build a node classifier on the learned embedding. To improve the discriminating power of the node embedding, we …
Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attri…
Proposes a hybrid model for stock market report classification using graph neural networks.
RR-GCN uses random transformations instead of learned weights for node embeddings.
New graph embedding method improves link prediction and node classification.