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.
Paper uses GNN and conformal prediction for accurate edge weight prediction.
New method uses Ricci curvature for hypergraph clustering, outperforming existing techniques.
A new discrete formula connects vertex and edge distributions on graphs.
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Su…
New method recovers transportable DAG structures from different datasets.
We develop a computationally efficient method to estimate Ollivier-Ricci curvature.
Review of modern computational optimal transport methods for biomedical applications.
Enhances graph comparison by incorporating edge features using Fused Gromov-Wasserstein distance.
Paper estimates non-causal graphical models using covariance extension and transportation distance.
NetOTC compares and aligns directed or undirected networks via random walk transitions.
We propose a new approach to graph compression by appeal to optimal transport. The transport problem is seeded with prior information about node importance, attributes, and edges in the graph. The transport formulation can be setup for either directed or undirected graphs, and its dual characterization is cast in terms…
Characterizes Forman curvature bounds and proves curvature equivalence.
While the backpropagation of error algorithm enables deep neural network training, it implies (i) bidirectional synaptic weight transport and (ii) update locking until the forward and backward passes are completed. Not only do these constraints preclude biological plausibility, but they also hinder the development of l…
We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to predict the flows on the remaining edges. To this end, we develop a computational framework that imposes certain constraints on the overall fl…
Dynamic transportation networks have been analyzed for years by means of static graph-based indicators in order to study the temporal evolution of relevant network components, and to reveal complex dependencies that would not be easily detected by a direct inspection of the data. This paper presents a state-of-the-art …
We study the problem of end-to-end learning from complex multigraphs with potentially very large numbers of edges between two vertices, each edge labeled with rich information. Examples range from communication networks to flights between airports or financial transaction graphs. We propose Latent-Graph Convolutional N…
The main results in this paper provide upper bounds of the second order Dehn functions for three-dimensional groups Nil and Sol. These upper bounds are obtained by using the Varopoulos transport argument on dual graphs. The first step is to start with reduced handlebody diagrams of the three-dimensional balls either im…
BWFlow improves graph generation by smoothly interpolating graph components.
This paper surveys algorithmic advancements in Optimal Transport with applications in machine learning.
A novel approach for semi-supervised learning using regularized optimal transport.
Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizi…
The paper tackles fairness in edge prediction for graphs, proposing a new method.
Framework for analyzing dynamic topological changes in point clouds using persistent homology and dynamic optimal transport.
To investigate the actual phenomena of transport on a complex network, we analysed empirical data for an inter-firm trading network, which consists of about one million Japanese firms and the sales of these firms (a sale corresponds to the total in-flow into a node). First, we analysed the relationships between sales a…
Structural and topological information play a key role in modeling flow and transport through fractured rock in the subsurface. Discrete fracture network (DFN) computational suites such as dfnWorks are designed to simulate flow and transport in such porous media. Flow and transport calculations reveal that a small back…
Deep learning compares turbulence models in plasma physics.
This work extends the randomized shortest paths (RSP) model by investigating the net flow RSP and adding capacity constraints on edge flows. The standard RSP is a model of movement, or spread, through a network interpolating between a random-walk and a shortest-path behavior [30, 42, 49]. The framework assumes a unit f…
Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe. Rapidly advancing vehicular communication and edge cloud computation technologies provide key enablers for smart traffic …
Optimal transport for measures on noisy tree metrics is solved with robust approach.
We consider point clouds obtained as random samples of a measure on a Euclidean domain. A graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points they connect. Our goal is to develop mathematical tools needed to study the consistency, as the number of availa…
PolyNSD improves Neural Sheaf Diffusion with polynomial operators and spectral rescaling.
Discrete forms of the scalar, sectional and Ricci curvatures are constructed on simplicial piecewise flat triangulations of smooth manifolds, depending directly on the simplicial structure and a choice of dual tessellation. This is done by integrating over volumes which include appropriate samplings of hinges for each …
PSO optimizes hyperparameters for edge ML models in FL.
DIF extends NF with stochastic discrete latent variables for better density estimation.
Proposes a new CNN for meshes that can handle orientation.
Graph Neural Networks and Guided Local Search improve TSP solutions.
By analysing the financial data of firms across Japan, a nonlinear power law with an exponent of 1.3 was observed between the number of business partners (i.e. the degree of the inter-firm trading network) and sales. In a previous study using numerical simulations, we found that this scaling can be explained by both th…
A new method for state estimation on complex networks.
We propose a scalable Gromov-Wasserstein learning (S-GWL) method and establish a novel and theoretically-supported paradigm for large-scale graph analysis. The proposed method is based on the fact that Gromov-Wasserstein discrepancy is a pseudometric on graphs. Given two graphs, the optimal transport associated with th…
New methods estimate transport-growth pairs in unbalanced optimal transport.
Introduces statistical optimal transport for probabilistic lectures.
Study shows how optimal transport behaves in higher dimensions.
A concise discussion of the axiomatic approach to the concept of parallel transport is presented. Attention is drawn to a bijective map between the sets of connections and (axiomatically defined) parallel transports. The transports along paths are pointed as a generalization of the (axiomatically defined) parallel tran…
The axiomatic approach to parallel transport theory is partially discussed. Bijective correspondences between the sets of connections, (axiomatically defined) parallel transports, and transports along paths satisfying some additional conditions, are constructed. In particular, the equivalence between the concepts "conn…
Networks are a fundamental model of complex systems throughout the sciences, and network datasets are typically analyzed through lower-order connectivity patterns described at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, also called network motifs, de…
New framework uses cohomology to analyze probabilistic distortions and arbitrage.
Under mild regularity assumptions, the transport problem is stable in the following sense: if a sequence of optimal transport plans converges weakly to a transport plan , then is also optimal (between its marginals). Alfonsi, Corbetta and Jourdain asked whether the same property is true for th…
NOT learns optimal transport plans, kernel costs improve performance.