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.
The paper studies a method to sample nodes from a massive graph using personalized PageRank.
Graph neural network optimizes energy-efficient precoding for massive MIMO systems.
AGNN improves network localization accuracy by 37-53% in NLOS conditions.
The article uses PageRank and persistent homology for scalable graph comparison.
Optimizes graph spectral density learning for large networks.
Study evaluates neural networks based on random graph structures and finds key performance indicators.
Deep learning approximates shortest path distances in large graphs.
New bootstraps improve speed and accuracy for graph count functionals.
The amount of personal data collected in our everyday interactions with connected devices offers great opportunities for innovative services fueled by machine learning, as well as raises serious concerns for the privacy of individuals. In this paper, we propose a massively distributed protocol for a large set of users …
Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges…
We present a novel condition, which we term the net- work nullspace property, which ensures accurate recovery of graph signals representing massive network-structured datasets from few signal values. The network nullspace property couples the cluster structure of the underlying network-structure with the geometry of th…
Linear time algorithm for random walk kernels on sparse graphs.
Paper proposes a new method for population-wise matching of sulcal graphs.
Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on this task. Due to their massive success, GNNs have attracted a lot of attention, and many novel architectures have been put forward. In thi…
Graphlets are induced subgraphs of a large network and are important for understanding and modeling complex networks. Despite their practical importance, graphlets have been severely limited to applications and domains with relatively small graphs. Most previous work has focused on exact algorithms, however, it is ofte…
Deep learning reduces training overhead in massive MIMO systems.
DNA rearrangement processes recombine gene segments that are organized on the chromosome in a variety of ways. The segments can overlap, interleave or one may be a subsegment of another. We use directed graphs to represent segment organizations on a given locus where contigs containing rearranged segments represent ver…
Massive fermions help understand index theorems without chiral symmetry.
A two-stage training method improves GNN graph classification accuracy.
Graph Neural Networks improve 3D object detection in LiDAR point clouds.
Deep nets with massive data learn spatially sparse functions.
Random orthogonalization improves FL in massive MIMO systems without CSI.
A new recommendation method combining deep learning and graph analysis improves performance.
The performance of classification algorithms with a massive and highly imbalanced data stream depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied in the past with Batch data to resolve the class imbalance problem. This paper proposes a new incremental data balancing framew…
Efficiently bootstraps massive distributed data without over-resampling.
Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties. However, the algorithms of such metrics are expensive in terms of computational resources when running real-time ap…
Many real-world large-scale regression problems can be formulated as Multi-task Learning (MTL) problems with a massive number of tasks, as in retail and transportation domains. However, existing MTL methods still fail to offer both the generalization performance and the scalability for such problems. Scaling up MTL met…
Graph connection Laplacian (GCL) is a modern data analysis technique that is starting to be applied for the analysis of high dimensional and massive datasets. Motivated by this technique, we study matrices that are akin to the ones appearing in the null case of GCL, i.e the case where there is no structure in the datas…
Paper proposes a novel GCN-based SSL algorithm to enhance node representations using contrastive and generative losses.
This paper tackles graph translation challenges by predicting both node and edge attributes simultaneously.
Many tasks in machine learning and data mining, such as data diversification, non-parametric learning, kernel machines, clustering etc., require extracting a small but representative summary from a massive dataset. Often, such problems can be posed as maximizing a submodular set function subject to a cardinality constr…
Efficient framework for robust training of GNNs against adversarial attacks.
A new criterion for deep active learning selects minimal labeled data points.
In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems. PathInf is a two-stages inference model. In the first stage, it applies a data summarization model based on maximum likelihood to …
Framework detects suspicious money laundering flows in large transaction graphs.
Networks are a natural representation of complex systems across the sciences, and higher-order dependencies are central to the understanding and modeling of these systems. However, in many practical applications such as online social networks, networks are massive, dynamic, and naturally streaming, where pairwise inter…
Study evaluates topological contributions in massive SQCD on compact 4-manifolds.
Massively multi-label prediction/classification problems arise in environments like health-care or biology where very precise predictions are useful. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, which results in few positive examples for t…
Unified framework for photon and massive particle hypersurfaces in stationary spacetimes.
This work proposes a novel method for semi-supervised learning from partially labeled massive network-structured datasets, i.e., big data over networks. We model the underlying hypothesis, which relates data points to labels, as a graph signal, defined over some graph (network) structure intrinsic to the dataset. Follo…
Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study. For these reasons, a more thorough and complete unde…
New algorithms for fair data summarization in massive data models.
In this work we simulate null geodesics for the Bonnor massive dipole metric by implementing a symbolic-numerical algorithm in Sage and Python. This program is also capable of visualizing in 3D, in principle, the geodesics for any given metric. Geodesics are launched from a common point, collectively forming a cone of …
FGN models networks with fractal structures using Gaussian Multiplicative Chaos.
Develops a new theory for approximating functions on massive data.
CSML learns causal structures for few-shot learning.
Introduces a massive variant of Ray-Singer Torsion to avoid zero modes in topological field theories.
New method for efficient inference in large datasets.