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.
GCN-LASE incorporates link attributes in graph convolutional networks.
DEAL model predicts links for new nodes with only attribute info.
Framework learns dynamic graph attributes and links co-evolution.
GG-SAGE predicts links in directed graphs with attributes, outperforming existing methods.
Generates missing node attributes for better graph-based tasks.
AUASE embeds dynamic networks with stability guarantees for node comparison.
Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational m…
DSGC improves graph representation learning by modeling object links and attribute relations.
The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circumstances in which nodes have an associated vector of continuous attributes that are also used to learn the node-…
Anomaly detection method separates contextual from behavioral attributes.
Estimates conversion probabilities from click sequences with privacy constraints.
A new model detects complex network communities using node attributes.
Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has b…
The paper tackles multi-level fairness in algorithmic systems, addressing bias at both individual and structural levels.
Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are ove…
GLACE embeds large-scale attributed graphs effectively, preserving structure and attributes.
We are concerned with modeling the strength of links in networks by taking into account how often those links are used. Link usage is a strong indicator of how closely two nodes are related, but existing network models in Bayesian Statistics and Machine Learning are able to predict only wether a link exists at all. As …
SpecAE detects anomalies in attributed networks by projecting them into a tailored space.
Nodes in real world networks often have class labels, or underlying attributes, that are related to the way in which they connect to other nodes. Sometimes this relationship is simple, for instance nodes of the same class are may be more likely to be connected. In other cases, however, this is not true, and the way tha…
DArtNet predicts time series data using graph structure and dynamic attributes.
AGE improves graph embedding by smoothing features and iteratively enhancing node embeddings.
Proposes a comprehensive framework for financial product lead recommendations using graph representation learning and link prediction.
Simple graph representation outperforms complex methods in graph classification.
New method predicts dynamic relationships in terrorist networks.
MGCN improves multi-layer graph classification using node attributes and relations.
Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applicatio…
Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both node at- tributes and network structure evolve under mutual influence. Specifically, we consider a mixed membership stochas…
Improved bipartite link prediction using 2-hop paths.
Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research. % It helps us infer the causality between two seemingly unrelated set of events. For example, if consumer spending in the United States declines, it has spillover effects on economies that de…
Optimal LDP mechanisms reduce data unfairness in classification.
Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain temporal information, the majority of existing techniques in relational learning focus on static snapshots and ignore the temporal dynamics. We propose…
Survey on deep learning for social network analysis.
We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix map…
Improves node classification in graphs with active learning.
Graphs (networks) are ubiquitous and allow us to model entities (nodes) and the dependencies (edges) between them. Learning a useful feature representation from graph data lies at the heart and success of many machine learning tasks such as classification, anomaly detection, link prediction, among many others. Many exi…
Multi-task learning (MTL) improves prediction performance in different contexts by learning models jointly on multiple different, but related tasks. Network data, which are a priori data with a rich relational structure, provide an important context for applying MTL. In particular, the explicit relational structure imp…
KGAT uses knowledge graphs to improve recommendation accuracy and explainability.
We present atomistic molecular dynamics simulations of two Polyethylene systems where all entanglements are trapped: a perfect network, and a melt with grafted chain ends. We examine microscopically at what level topological constraints can be considered as a collective entanglement effect, as in tube model theories, o…
New method detects novel node categories in graphs with distribution shifts.
We present an analysis of the credit market of Japan. The analysis is performed by investigating the bipartite network of banks and firms which is obtained by setting a link between a bank and a firm when a credit relationship is present in a given time window. In our investigation we focus on a community detection alg…
Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as lin…
The study examines how board diversity and CSR committee composition affect corporate governance and financial performance.
New model predicts links in community-based networks robustly.
New satellite knots counter a conjecture about Lorenz knots.
FairDrop improves fairness in graph representation learning by counteracting homophily.
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible a…
Develops new methods to evaluate data influence in SAM for improved model training.
Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can b…