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.
Framework learns dynamic graph attributes and links co-evolution.
New method separates graph structure from node attributes to recover lost signal.
Proposes methods for local clustering in attributed graphs.
WGNN learns graph representations from incomplete attribute data.
A-DOGE embeds attributed graphs efficiently using density of states.
Graph structured data provide two-fold information: graph structures and node attributes. Numerous graph-based algorithms rely on both information to achieve success in supervised tasks, such as node classification and link prediction. However, node attributes could be missing or incomplete, which significantly deterio…
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well. To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with…
Develops GNNs for incomplete graphs, improving learning from missing node attributes.
SASE improves attributed graph clustering for large graphs with linear time and space complexity.
Graph neural networks (GNNs) have shown great power in learning on attributed graphs. However, it is still a challenge for GNNs to utilize information faraway from the source node. Moreover, general GNNs require graph attributes as input, so they cannot be appled to plain graphs. In the paper, we propose new models nam…
Enhances community detection in correlated networks with node attributes.
Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content information, and several recent methods based on it have achieved promisin…
Improved graph clustering with modularity and coarsening for attributes and communities.
Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes. Low-rank proximities and node features are successfully leveraged in existing GCNs, however, attributes that graph links may carry are commonly ignored, as almost …
We propose a simple yet effective method for detecting anomalous instances on an attribute graph with label information of a small number of instances. Although with standard anomaly detection methods it is usually assumed that instances are independent and identically distributed, in many real-world applications, inst…
We consider the clustering problem of attributed graphs. Our challenge is how we can design an effective and efficient clustering method that precisely captures the hidden relationship between the topology and the attributes in real-world graphs. We propose Non-linear Attributed Graph Clustering by Symmetric Non-negati…
Graph embedding methods transform high-dimensional and complex graph contents into low-dimensional representations. They are useful for a wide range of graph analysis tasks including link prediction, node classification, recommendation and visualization. Most existing approaches represent graph nodes as point vectors i…
This paper tackles graph translation challenges by predicting both node and edge attributes simultaneously.
Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurrent Neural Network (GraphVRNN), a probabilistic autoregressive model for graph generation. Through modeling the latent variables of graph data…
Unified analysis for graph learning from multi-attribute Gaussian time series.
A framework for hypothesis testing on attributed graphs using sampling.
Paper defends sensitive attributes in GNNs from inference attacks.
Many real world network problems often concern multivariate nodal attributes such as image, textual, and multi-view feature vectors on nodes, rather than simple univariate nodal attributes. The existing graph estimation methods built on Gaussian graphical models and covariance selection algorithms can not handle such d…
We propose graph kernels based on subgraph matchings, i.e. structure-preserving bijections between subgraphs. While recently proposed kernels based on common subgraphs (Wale et al., 2008; Shervashidze et al., 2009) in general can not be applied to attributed graphs, our approach allows to rate mappings of subgraphs by …
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
Unified analysis of multi-attribute graph learning with non-convex penalties.
Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications. Graph structure optimization, aiming to find the optimal graphs in terms of some specific measures, has become an effective computational tool…
Graphs are complex objects that do not lend themselves easily to typical learning tasks. Recently, a range of approaches based on graph kernels or graph neural networks have been developed for graph classification and for representation learning on graphs in general. As the developed methodologies become more sophistic…
Graph Prototypical Networks improve few-shot node classification on attributed networks.
New method predicts dynamic relationships in terrorist networks.
Auto-encoders have emerged as a successful framework for unsupervised learning. However, conventional auto-encoders are incapable of utilizing explicit relations in structured data. To take advantage of relations in graph-structured data, several graph auto-encoders have recently been proposed, but they neglect to reco…
PathBoost boosts graph-level predictions using path-based features.
Paper analyzes multi-attribute data to estimate differences in Gaussian graphical models.
GG-SAGE predicts links in directed graphs with attributes, outperforming existing methods.
Graph Kalman filters adapt classical filters to graph data.
Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that computes the representation of a node by aggregating those of its neighbors. However, existing GCN variants commonly use 1-D graph convolution …
GUIDE detects anomalies in attributed networks by reconstructing node attributes and higher-order structures.
In many graphs such as social networks, nodes have associated attributes representing their behavior. Predicting node attributes in such graphs is an important problem with applications in many domains like recommendation systems, privacy preservation, and targeted advertisement. Attributes values can be predicted by a…
New framework for disentangling graph node and edge features.
Hierarchical Latent Attribute Models (HLAMs) are a family of discrete latent variable models that are attracting increasing attention in educational, psychological, and behavioral sciences. The key ingredients of an HLAM include a binary structural matrix and a directed acyclic graph specifying hierarchical constraints…
AGE improves graph embedding by smoothing features and iteratively enhancing node embeddings.
GPT-GNN pre-trains GNNs on unlabeled graphs to improve downstream performance.
DArtNet predicts time series data using graph structure and dynamic attributes.
Tree Mover's Distance measures graph attributes and improves GNN performance.
DEAL model predicts links for new nodes with only attribute info.
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 …
Algorithm uncovers latent attribute graph from molecular data.
A novel algorithm for unsupervised graph representation learning combining coarsening and mutual information maximization.