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.
GLAD improves latent graph generation by quantizing discrete latent space.
New method learns graph structure and uncertainty from data.
Deep NLP models benefit from underlying structures in the data---e.g., parse trees---typically extracted using off-the-shelf parsers. Recent attempts to jointly learn the latent structure encounter a tradeoff: either make factorization assumptions that limit expressiveness, or sacrifice end-to-end differentiability. Us…
The paper predicts responses on out-of-sample nodes using latent positions on unknown curves.
A new model clusters networks with community-specific submanifold structures.
Auto-decoder synthesizes graphs from latent codes.
Paper distinguishes causal structures under latent confounding and selection bias.
DCRL learns causal relationships from mixed-type discrete data.
GraphOpt learns the formation mechanism of graphs from observed structures.
This paper shows how to estimate distances in latent space of random graphs using entropic OT.
GLFA improves latent factor analysis by incorporating graph structures for HiDS matrices.
AMES framework selects optimal embedding space for latent graph inference.
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…
Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred. To predict whether a relation holds between entities, embeddings are typically compared in the latent space following a relation-spe…
We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model us…
Paper presents a novel approach for global feature aggregation in Graph Neural Networks.
Generative model for creating graphs with new communities.
Tiered latent representations and latent spaces for molecular graphs provide a simple but effective way to explicitly represent and utilize groups (e.g., functional groups), which consist of the atom (node) tier, the group tier and the molecule (graph) tier. They can be learned using the tiered graph autoencoder archit…
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…
Paper shows graphs can be embedded in lower dimensions than expected.
We provide a theoretical analysis of the representation learning problem aimed at learning the latent variables (design matrix) of observations with the knowledge of the coefficient matrix . The design matrix is learned under the assumption that the latent variables are smooth with respect to a (known) t…
Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant. In this paper, we develop a novel hierarchical variational model that introduces additional latent random variables to jointly model the hidden states of a graph…
Enhances graph modeling with hyperbolic geometry and variational inference.
From social networks to Internet applications, a wide variety of electronic communication tools are producing streams of graph data; where the nodes represent users and the edges represent the contacts between them over time. This has led to an increased interest in mechanisms to model the dynamic structure of time-var…
Study reconstructs causal graph from latent variables using mixture oracles.
Framework generates causal probabilities from observational data.
Paper proposes a new method to identify causal graphs with latent variables using higher-order cumulants.
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the laten…
Study designs experiments to identify causal graph structure with cycles and latent confounders.
IDPGs extend RDPGs with a Poisson process for random latent positions.
Proposes a model combining graph networks and variational Bayes for graph data.
iGCL preserves graph semantics in latent space augmentations.
Convex optimization method infers latent structure in random dot product graphs.
We provide the first information theoretic tight analysis for inference of latent community structure given a sparse graph along with high dimensional node covariates, correlated with the same latent communities. Our work bridges recent theoretical breakthroughs in the detection of latent community structure without no…
DGA and DVGA learn disentangled graph representations to improve graph analysis.
EVGAE improves VGAE's latent representation learning by mitigating over-pruning.
Generative models of graph structure have applications in biology and social sciences. The state of the art is GraphRNN, which decomposes the graph generation process into a series of sequential steps. While effective for modest sizes, it loses its permutation invariance for larger graphs. Instead, we present a permuta…
New method uses manifold learning to infer latent positions of 1D submanifolds in random dot product graphs.
Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspecified model, or equivalently, an incorrect inference algorithm will result in an invalid analysis or even falsely uncover patterns that are in fact artifacts of the model. This work focuses on uni…
Graph structured data are abundant in the real world. Among different graph types, directed acyclic graphs (DAGs) are of particular interest to machine learning researchers, as many machine learning models are realized as computations on DAGs, including neural networks and Bayesian networks. In this paper, we study dee…
Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the community structure of the network, with strong consistency results in the stoch…
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…
Paper proposes a fast algorithm to recover causal DAGs with latent variables.
GCNs learn by embedding similar nodes within a class and leveraging consistent neighborhood structures.
Proposes a new model for directed graphs combining deep learning and latent variable models.
A Bayesian treatment of latent directed graph structure for non-iid data is provided where each child datum is sampled with a directed conditional dependence on a single unknown parent datum. The latent graph structure is assumed to lie in the family of directed out-tree graphs which leads to efficient Bayesian inferen…
Develops SCMs for latent selection to simplify causal analysis.
We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities. We introduce σ-connection graphs (σ-CG), a new class of mixed graphs (containing undirected, bidirected…