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.
LC-GNN improves GNNs for node classification by incorporating label consistency.
We present a method to estimate block membership of nodes in a random graph generated by a stochastic blockmodel. We use an embedding procedure motivated by the random dot product graph model, a particular example of the latent position model. The embedding associates each node with a vector; these vectors are clustere…
The paper proves ML estimators are strongly consistent for identifying edge weights in BAR models.
New decentralized KRR algorithm adapts to node-specific data.
This paper improves spectral embedding for multipartite networks, revealing latent subspaces and providing consistent node representations.
Study on predicting graph labels at nodes using local averaging and distance estimation.
GCNs learn by embedding similar nodes within a class and leveraging consistent neighborhood structures.
We propose a method to identify all the nodes that are relevant to compute all the conditional probability distributions for a given set of nodes. Our method is simple, effcient, consistent, and does not require learning a Bayesian network first. Therefore, our method can be applied to high-dimensional databases, e.g. …
ARGEW improves node embeddings for weighted homophilous graphs by emphasizing strong edge weights.
Implementing -NN classification using Gromov--Wasserstein distances
A graph clustering method that moves nodes to highest-degree neighbors.
New method estimates graphons from multiple networks with high accuracy and low complexity.
Paper improves BN structure learning from incomplete data.
AGE improves graph embedding by smoothing features and iteratively enhancing node embeddings.
Develops a new variational estimator for node popularity in bipartite networks.
DiMMSB models directed mixed membership networks, identifying distinct community structures.
We consider the problem of estimating the latent structure of a social network based on the observed information diffusion events, or cascades, where the observations for a given cascade consist of only the timestamps of infection for infected nodes but not the source of the infection. Most of the existing work on this…
We present an algorithm for classification tasks on big data. Experiments conducted as part of this study indicate that the algorithm can be as accurate as ensemble methods such as random forests or gradient boosted trees. Unlike ensemble methods, the models produced by the algorithm can be easily interpreted. The algo…
New method clusters multilayer graphs with missing nodes.
This work examines the problem of graph learning over a diffusion network when data can be collected from a limited portion of the network (partial observability). The main question is to establish technical guarantees of consistent recovery of the subgraph of probed network nodes, i) despite the presence of unobserved…
We present a method for the reconstruction of networks, based on the order of nodes visited by a stochastic branching process. Our algorithm reconstructs a network of minimal size that ensures consistency with the data. Crucially, we show that global consistency with the data can be achieved through purely local consid…
This paper tackles graph translation challenges by predicting both node and edge attributes simultaneously.
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…
In many application settings involving networks, such as messages between users of an on-line social network or transactions between traders in financial markets, the observed data consist of timestamped relational events, which form a continuous-time network. We propose the Community Hawkes Independent Pairs (CHIP) ge…
Graph neural networks (GNNs) have received much attention recently because of their excellent performance on graph-based tasks. However, existing research on GNNs focuses on designing more effective models without considering much about the quality of the input data. In this paper, we propose self-enhanced GNN (SEG), w…
The paper proposes a new model to analyze directed networks and accurately estimate community memberships.
A simple banking network model is proposed which features multiple waves of bank defaults and is analytically solvable in the limiting case of an infinitely large homogeneous network. The model is a collection of nodes representing individual banks; associated with each node is a balance sheet consisting of assets and …
Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existing graph. Despite the emerging literature in learning graph representation and graph generation, most of them can not handle isolated new nod…
Proposes a Bayesian approach for automatic node selection in sparse neural networks.
GEM detects malicious accounts using adaptive embeddings from heterogeneous graphs.
Graph Neural Networks (GNNs) are efficient approaches to process graph-structured data. Modelling long-distance node relations is essential for GNN training and applications. However, conventional GNNs suffer from bad performance in modelling long-distance node relations due to limited-layer information propagation. Ex…
InstantEmbedding efficiently generates node representations with less computation and memory.
Graph Random Neural Network improves semi-supervised learning on graphs.
Paper proposes JDR to denoise graph features and rewire graphs for better node classification.
Directed graphs have asymmetric connections, yet the current graph clustering methodologies cannot identify the potentially global structure of these asymmetries. We give a spectral algorithm called di-sim that builds on a dual measure of similarity that correspond to how a node (i) sends and (ii) receives edges. Using…
New algorithm improves hypergraph clustering for unbalanced communities.
DEAL model predicts links for new nodes with only attribute info.
A method to control results of gradient descent unsupervised learning in a deep neural network by using evolutionary algorithm is proposed. To process crossover of unsupervisedly trained models, the algorithm evaluates pointwise fitness of individual nodes in neural network. Labeled training data is randomly sampled an…
Boost GNNs for node classification by incorporating label dependencies.
PanRep learns universal node embeddings for heterogeneous graphs.
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, most existing methods neglect the complex cross-modality interactio…
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…
Bayesian Neural Networks (BNNs) have recently received increasing attention for their ability to provide well-calibrated posterior uncertainties. However, model selection---even choosing the number of nodes---remains an open question. Recent work has proposed the use of a horseshoe prior over node pre-activations of a …
This paper shows how to estimate distances in latent space of random graphs using entropic OT.
We report a scalable hybrid quantum-classical machine learning framework to build Bayesian networks (BN) that captures the conditional dependence and causal relationships of random variables. The generation of a BN consists of finding a directed acyclic graph (DAG) and the associated joint probability distribution of t…
Consistent spectral clustering with fairness constraints on representation graphs.
Proposes MGMN for end-to-end graph similarity learning.
SCNode improves node embeddings for GNNs in both homophilic and heterophilic graphs.