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.
EvoNet predicts the evolution of dynamic graphs using a graph neural network and recurrent architecture.
The paper extends a spectral evolution model for link prediction in evolving networks.
Study hexagonal network evolution under curvature flow.
New framework analyzes belief evolution in social networks.
We investigate topology and temporal evolution of the foreign currency exchange market viewed from a weighted network perspective. Based on exchange rates for a set of 46 currencies (including precious metals), we construct different representations of the FX network depending on a choice of the base currency. Our resu…
We consider the evolution by curvature of a general embedded network with two triple junctions. We classify the possible singularities and we discuss the long time existence of the evolution.
Graph neural network using Beltrami flow for feature and topology evolution.
DeepStreamCE detects new classes in streaming deep neural networks.
We consider a regular embedded network composed by two curves, one of them closed, in a convex domain . The two curves meet only in one point, forming angle of degrees. The non-closed curve has a fixed end point on . We study the evolution by curvature of this network. We show that the maximal exist…
Anisotropic curvature flow studied for planar networks.
The paper proposes an evolution-based approach to estimate causal effects in interference networks without fully observing the network structure.
A promising paradigm for achieving highly efficient deep neural networks is the idea of evolutionary deep intelligence, which mimics biological evolution processes to progressively synthesize more efficient networks. A crucial design factor in evolutionary deep intelligence is the genetic encoding scheme used to simula…
Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can b…
New algorithm assesses credit risk in multilayer networks over time.
Network embedding aims to embed nodes into a low-dimensional space, while capturing the network structures and properties. Although quite a few promising network embedding methods have been proposed, most of them focus on static networks. In fact, temporal networks, which usually evolve over time in terms of microscopi…
DANR improves network regularization for spatio-temporal data.
We model how Lipschitz continuity changes during neural network training.
Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly i…
Differentiable NAS frameworks grow networks wider and deeper, revealing biases in wiring evolution.
In automatic financial feature construction task, the state-of-the-art technic leverages reverse polish expression to represent the features, then use genetic programming (GP) to conduct its evolution process. In this paper, we propose a new framework based on neural network, alpha discovery neural network (ADNN). In t…
We exploit the symmetry concepts developed in the companion review of this article to introduce a stochastic version of link reversal symmetry, which leads to an improved understanding of the reciprocity of directed networks. We apply our formalism to the international trade network and show that a strong embedding in …
Framework learns dynamic graph attributes and links co-evolution.
Paper uses RNNs to design LDPC codes for binary erasure channels.
Gradient descent converges to perfect classification in neural nets for non-separable data.
We develop a simple theoretical framework for the evolution of weighted networks that is consistent with a number of stylized features of real-world data. In our framework, the Barabasi-Albert model of network evolution is extended by assuming that link weights evolve according to a geometric Brownian motion. Our model…
Study on network flow singularities, focusing on Type-0 singularities.
Buyer--seller relationships among firms can be regarded as a longitudinal network in which the connectivity pattern evolves as each firm receives productivity shocks. Based on a data set describing the evolution of buyer--seller links among 55,608 firms over a decade and structural equation modeling, we find some evide…
Simplicial persistence measures financial market dynamics, revealing long-term structure evolution.
We present a framework for recovering/approximating unknown time-dependent partial differential equation (PDE) using its solution data. Instead of identifying the terms in the underlying PDE, we seek to approximate the evolution operator of the underlying PDE numerically. The evolution operator of the PDE, defined in i…
In this note we study the bilateral merchandise trade flows between 186 countries over the 1948-2005 period using data from the International Monetary Fund. We use Pajek to identify network structure and behavior across thresholds and over time. In particular, we focus on the evolution of trade "islands" in the a world…
New method predicts dynamic relationships in terrorist networks.
CoMGNN models heterogeneous graphs with evolving nodes and edges.
We consider the motion by curvature of a network of curves in the plane and we discuss existence, uniqueness, singularity formation and asymptotic behavior of the flow.
Model predicts time evolution of supply chain networks under varying costs.
We present a collection of results on the evolution by curvature of networks of planar curves. We discuss in particular the existence of a solution and the analysis of singularities.
Financial networks have become extremely useful in characterizing the structure of complex financial systems. Meanwhile, the time evolution property of the stock markets can be described by temporal networks. We utilize the temporal network framework to characterize the time-evolving correlation-based networks of stock…
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the sociability of that node. Sociabilities are assumed to evolve over time, and are modelled via a dynamic point process model. The model is a…
This paper studies the critical dynamics of random surfaces, focusing on area and genus evolution.
Study on neural networks with regularisation and its impact on training dynamics.
A new framework explains why early pruning works well.
Study shows institutional investments significantly impact cryptocurrency market evolution.
Generative model predicts remaining life of damaged structures.
The paper uses neural networks to forecast time series data.
In the brain, learning signals change over time and synaptic location, and are applied based on the learning history at the synapse, in the complex process of neuromodulation. Learning in artificial neural networks, on the other hand, is shaped by hyper-parameters set before learning starts, which remain static through…
Our knowledge about the evolution of guarantee network in downturn period is limited due to the lack of comprehensive data of the whole credit system. Here we analyze the dynamic Chinese guarantee network constructed from a comprehensive bank loan dataset that accounts for nearly 80% total loans in China, during 01/200…
Analyzes feature learning in neural networks using a self-consistent dynamical field theory.
A new neural network approach for diffusion on networks.
Proposes DTS framework to predict CTR by tracking user interest evolution over time.