arXiv subjects

Yunyan Yang

Publications and source records attributed to Yunyan Yang.

At least 19 recordsLinked to original sources

arXiv2026

Evolution of weights on a connected finite graph

On a connected finite graph, we propose an evolution of weights including Ollivier's Ricci flow as a special case. During the evolution process, on each edge, the speed of change of weight is exactly the difference between the Wasserstein distance related to two probability measures and certain graph distance. Here the probability measure may be chosen as an $α$-lazy one-step random walk, an $α$-lazy two-step random walk, or a general probability measure. Based on the ODE theory, we show that the initial value problem has a unique global solution. A discrete version of the above evolution is applied to the problem of community detection. Our algorithm is based on such a discrete evolution, where probability measures are chosen as $α$-lazy one-step random walk and $α$-lazy two-step random walk respectively. Note that the latter measure has not been used in previous works. Moreover, only one surgery needs to be performed after the last iteration, which makes our algorithm much simpler than earlier ones based on Lin-Lu-Yau's Ricci curvature.

math.CA↗

arXiv2026

CurvFlow-DTA: dual-graph discrete Ricci curvature flow for drug--target affinity prediction

Graph neural networks are widely used for drug--target affinity (DTA) prediction, and discrete Ricci curvature has recently been used to characterize molecular graph geometry. Existing curvature-aware DTA approaches mainly use static curvature on the drug graph while representing proteins primarily with sequence-derived features. This leaves pair-adaptive use of graph geometry underexplored, which may limit adaptation to unseen entities in cold-start settings relevant to practical screening. We present CurvFlow-DTA, which replaces a single static curvature representation with weighted Forman curvature flow on both molecular and protein residue--residue contact graphs. A label-independent flow trajectory is precomputed for each entity, and a pair-conditioned selector determines the horizons read by a dual-branch Flow-GINE. A frozen ESM-2 supplies residue-level representations and contact scores used to construct the protein graph. Inference requires only SMILES strings and protein sequences, without a bound complex structure. On Davis and KIBA, CurvFlow-DTA improves on the protocol-matched Ricci-GraphDTA baseline in every warm and cold-start setting. Warm-split mean squared error (MSE) decreases by $19.9\%$ on Davis and $18.9\%$ on KIBA. Across the six cold-start comparisons, MSE decreases by $14.3$--$27.4\%$, with higher concordance index (CI) throughout. Within our compiled set of literature baselines, CurvFlow-DTA achieves the lowest MSE on both warm benchmarks and across four out of six cold-start evaluation settings.

cs.LG↗

arXiv2026

Equivalence of Lin--Lu--Yau curvature and 1/2-Ollivier curvature on weighted graphs

In this note, we prove that, on weighted graphs, the Lin--Lu--Yau curvature coincides with the $p$-Ollivier curvature up to scaling whenever the idleness parameter $p\geq 1/2$. Moreover, the threshold $1/2$ is sharp. This extends an earlier result of Bourne et al. (Ollivier--Ricci idleness functions of graphs, SIAM J. Discrete Math., 32 (2018), no. 2, 1408-1424), where combinatorial graphs were considered. This observation yields a simple proof for the global existence and uniqueness of solutions of the Lin--Lu--Yau curvature flow in Bai et al. (Ollivier Ricci-flow on weighted graphs, Amer. J. Math. 146 (2024), 1723-1747).

math.DG↗

arXiv2026

Evolving edge weights via local entropy flow and cohesion flow on graphs

In this paper, we first propose two different quantities on graphs, namely local entropy and cohesion, then design two corresponding flows for edge weights: the local entropy flow and the cohesion flow. We establish the global existence and uniqueness of solutions for both flows and investigate their asymptotic behaviors, including the case that the limit goes to positive infinity. Moreover, they can be applied to fundamental network analysis tasks, including community detection and node classification. Empirical evaluations demonstrate that our method achieves performance competitive with Ollivier Ricci flow and Lin-Lu-Yau Ricci flow on benchmark network analysis tasks. In experimental scenarios, we first apply the cohesion flow to evolve the edge weights of the graph, and then apply the local entropy flow to further update the resulting weighted graph. Both flows are computationally efficient, leading to a significant reduction in overall computational cost and improved scalability.

math.CA↗

arXiv2026

Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow

We introduce the Geometric Evolution Graph Convolutional Network (GEGCN), a novel framework that enhances graph representation learning through explicit modeling of geometric evolution on graph structures. Specifically, GEGCN leverages a Long Short-Term Memory (LSTM) network to capture the dynamic structural sequence generated by discrete Ricci flow, and infuses the learned dynamic representations into a graph convolutional network. Extensive experiments demonstrate that GEGCN achieves excellent performance on classification tasks across various benchmark datasets, including homophilic/heterophilic graphs, filtered graphs, and large-scale graphs.

cs.LG↗

arXiv2026

An Efficient Entropy Flow on Weighted Graphs: Theory and Applications

We propose a novel entropy flow on weighted graphs, which provides a principled framework that characterizes the evolution of probability distributions over graph structures while sharing geometric intuition with discrete Ricci flow. We provide its rigorous formulation, establish its fundamental theoretical properties, and prove the long-time existence and convergence of its solutions. To demonstrate its applicability, we employ entropy flow for community detection in real-world networks. Empirically, it achieves detection accuracy fully comparable to that of discrete Ricci flow. Crucially, by avoiding computations of optimal transport distances and shortest paths, our approach overcomes the fundamental computational bottleneck of Ollivier and Lin-Lu-Yau Ricci flows. As a result, entropy flow requires only $1.61\%$-$3.20\%$ of the computation time of Ricci flow. These results indicate that entropy flow provides a theoretically rigorous and computationally efficient framework for large-scale graph analysis.

math.CA↗

arXiv2026

Core detection via Ricci curvature flows on weighted graphs

Graph Ricci curvature is crucial as it geometrically quantifies network structure. It pinpoints bottlenecks via negative curvature, identifies cohesive communities with positive curvature, and highlights robust hubs. This guides network analysis, resilience assessment, flow optimization, and effective algorithm design. In this paper, we derived upper and lower bounds for the weights along several kinds of discrete Ricci curvature flows. As an application, we utilized discrete Ricci curvature flows to detect the core subgraph of a finite undirected graph. The novelty of this work has two aspects. Firstly, along the Ricci curvature flow, the bounds for weights determine the minimum number of iterations required to ensure weights remain between two prescribed positive constants. In particular, for any fixed graph, we conclude weights can not overflow and can not be treated as zero, as long as the iteration does not exceed a certain number of times; Secondly, it demonstrates that our Ricci curvature flow method for identifying core subgraphs outperforms prior approaches, such as page rank, degree centrality, betweenness centrality and closeness centrality. The codes for our algorithms are available at https://github.com/12tangze12/core-detection-via-Ricci-flow.

math.AP↗

arXiv2025

Finding core subgraphs of directed graphs via discrete Ricci curvature flow

Ricci curvature and its associated flow offer powerful geometric methods for analyzing complex networks. While existing research heavily focuses on applications for undirected graphs such as community detection and core extraction, there have been relatively less attention on directed graphs. In this paper, we introduce a definition of Ricci curvature and an accompanying curvature flow for directed graphs. Crucially, for strongly connected directed graphs, this flow admits a unique global solution. We then apply this flow to detect strongly connected subgraphs from weakly connected directed graphs. (A weakly connected graph is connected overall but not necessarily strongly connected). Unlike prior work requiring graphs to be strongly connected, our method loosens this requirement. We transform a weakly connected graph into a strongly connected one by adding edges with very large artificial weights. This modification does not compromise our core subgraph detection. Due to their extreme weight, these added edges are automatically discarded during the final iteration of the Ricci curvature flow. For core evaluation, our approach consistently surpasses traditional methods, achieving better results on at least two out of three key metrics. The implementation code is publicly available at https://github.com/12tangze12/Finding-core-subgraphs-on-directed-graphs.

cs.SI↗

arXiv2025

A modified Ricci flow on arbitrary weighted graph

In this paper, we propose a modified Ricci flow, as well as a quasi-normalized Ricci flow, on arbitrary weighted graph. Each of these two flows has a unique global solution. In particular, these global existence and uniqueness results do not require an exit condition proposed by Bai et al in a recent work [2]. As applications, these two Ricci flows are applied to community detection for complex networks, including Karate Club, American football games, Facebook, as well as artificial networks. In our algorithms, unlike in [5,15], there is no need to perform surgery at every iteration, only one surgery needs to be performed after the last iteration. From three commonly used criteria for evaluating community detection algorithms, ARI, NMI and Q, we conclude that our algorithms outperform existing algorithms, including Ollivier's Ricci flow [5], normalized Ollivier's Ricci flow and normalized Lin-Lu-Yau's Ricci flow [15]. The codes for our algorithms are available at https://github.com/mjc191812/Modified-Ricci-Flow.

math.AP↗

arXiv2025

Fractional Laplace operator and related Schrödinger equations on locally finite graphs

In this paper, we first define a discrete version of the fractional Laplace operator $(-Δ)^{s}$ through the heat semigroup on a stochastically complete, connected, locally finite graph $G = (V, E, μ, w)$. Secondly, we define the fractional divergence and give another form of $(-Δ)^s$. The third point, and the foremost, is the introduction of the fractional Sobolev space $W^{s,2}(V)$, which is necessary when we study problems involving $(-Δ)^{s}$. Finally, using the mountain-pass theorem and the Nehari manifold, we obtain multiplicity solutions to a discrete fractional Schrödinger equation on $G$. We caution the readers that though these existence results are well known in the continuous case, the discrete case is quite different.

math.AP↗

arXiv2025

Fractional Sobolev spaces and fractional $p$-Laplace equations on locally finite graphs

Graph-based analysis holds both theoretical and applied significance, attracting considerable attention from researchers and yielding abundant results in recent years. However, research on fractional problems remains limited, with most of established results restricted to lattice graphs. In this paper, fractional Sobolev spaces are constructed on general graphs that are connected, locally finite and stochastically complete. Under certain assumptions, these spaces exhibit completeness, reflexivity, and other properties. Moreover, we propose a fractional $p$-Laplace operator, and study the existence of solutions to some nonlinear Schrödinger type equations involving this nonlocal operator. The main contribution of this paper is to establish a relatively comprehensive set of analytical tools for studying fractional problems on graphs.

math.AP↗

arXiv2025

Piecewise-linear Ricci curvature flows on weighted graphs

Community detection is an important problem in graph neural networks. Recently, algorithms based on Ricci curvature flows have gained significant attention. It was suggested by Ollivier (2009), and applied to community detection by Ni et al (2019) and Lai et al (2022). Its mathematical theory was due to Bai et al (2024) and Li-Münch (2025). In particular, solutions to some of these flows have existence, uniqueness and convergence. However, a unified theoretical framework has not yet been established in this field. In the current study, we propose several unified piecewise-linear Ricci curvature flows with respect to arbitrarily selected Ricci curvatures. First, we prove that the flows have global existence and uniqueness. Second, we show that if the Ricci curvature being used is homogeneous, then after undergoing multiple surgeries, the evolving graph has a constant Ricci curvature on each connected component. Note that five commonly used Ricci curvatures, which were respectively defined by Ollivier, Lin-Lu-Yau, Forman, Menger and Haantjes, are all homogeneous, and that the proof of all these results is independent of the choice of the specific Ricci curvature. Third, as an application, we apply the discrete piecewise-linear Ricci curvature flow with surgeries to the problem of community detection. On three real-world datasets, the flow consistently outperforms baseline models and existing methods. Complementary experiments on synthetic graphs further confirm its scalability and robustness. Compared with existing algorithms, our algorithm has two advantages: it does not require curvature calculations at each iteration, and the iterative process converges.

math.AP↗

arXiv2025

Community detection of hypergraphs by Ricci flow

Community detection in hypergraphs is both instrumental for functional module identification and intricate due to higher-order interactions among nodes. We define a hypergraph Ricci flow that directly operates on higher-order interactions of hypergraphs and prove long-time existence of the flow. Building on this theoretical foundation, we develop HyperRCD-a Ricci-flow-based community detection approach that deforms hyperedge weights through curvature-driven evolution, which provides an effective mathematical representation of higher-order interactions mediated by weighted hyperedges between nodes. Extensive experiments on both synthetic and real-world hypergraphs demonstrate that HyperRCD exhibits remarkable enhanced robustness to topological variations and competitive performance across diverse datasets.

cs.SI↗

arXiv2025

Nonlinear Diffusion Equations on Graphs: Global Well-Posedness, Blow-Up Analysis and Applications

For a nonlinear diffusion equation on graphs whose nonlinearity violates the Lipschitz condition, we prove short-time solution existence and characterize global well-posedness by establishing sufficient criteria for blow-up phenomena and quantifying blow-up rates. These theoretical results are then applied to model complex dynamical networks, with supporting numerical experiments. This work mainly makes two contributions: (i) generalization of existing results for diffusion equations on graphs to cases with nontrivial potentials, producing richer analytical results; (ii) a new PDE approach to model complex dynamical networks, with preliminary numerical experiments confirming its validity.

math.AP↗

arXiv2025

Fractional Laplace operator on finite graphs

Nowadays a great attention has been focused on the discrete fractional Laplace operator as the natural counterpart of the continuous one. In this paper, we discretize the fractional Laplace operator $(-Δ)^{s}$ for an arbitrary finite graph and any positive real number $s$. It is shown that $(-Δ)^{s}$ can be explicitly represented by eigenvalues and eigenfunctions of the Laplace operator $-Δ$. Moreover, we study its important properties, such as $(-Δ)^{s}$ converges to $-Δ$ as $s$ tends to $1$; while $(-Δ)^{s}$ converges to the identity map as $s$ tends to $0$ on a specific function space. For related problems involving the fractional Laplace operator, we consider the fractional Kazdan-Warner equation and obtain several existence results via variational principles and the method of upper and lower solutions.

math.AP↗

arXiv2023

Topological degree for Chern-Simons Higgs models on finite graphs

Let $(V,E)$ be a finite connected graph. We are concerned about the Chern-Simons Higgs model $$Δu=λe^u(e^u-1)+f, \quad\quad\quad\quad\quad\quad{(0.1)}$$ where $Δ$ is the graph Laplacian, $λ$ is a real number and $f$ is a function on $V$. When $λ>0$ and $f=4π\sum_{i=1}^Nδ_{p_i}$, $N\in\mathbb{N}$, $p_1,\cdots,p_N\in V$, the equation (0.1) was investigated by Huang, Lin, Yau (Commun. Math. Phys. 377 (2020) 613-621) and Hou, Sun (Calc. Var. 61 (2022) 139) via the upper and lower solutions principle. We now consider an arbitrary real number $λ$ and a general function $f$, whose integral mean is denoted by $\overline{f}$, and prove that when $λ\overline{f}<0$, the equation $(0.1)$ has a solution; when $λ\overline{f}>0$, there exist two critical numbers $Λ^\ast>0$ and $Λ_\ast<0$ such that if $λ\in(Λ^\ast,+\infty)\cup(-\infty,Λ_\ast)$, then $(0.1)$ has at least two solutions, including one local minimum solution; if $λ\in(0,Λ^\ast)\cup(Λ_\ast,0)$, then $(0.1)$ has no solution; while if $λ=Λ^\ast$ or $Λ_\ast$, then $(0.1)$ has at least one solution. Our method is calculating the topological degree and using the relation between the degree and the critical group of a related functional. Similar method is also applied to the Chern-Simons Higgs system, and a partial result for the multiple solutions of the system is obtained.

math.AP↗

arXiv2023

Existence and convergence of solutions to $p$-Laplace equations on locally finite graphs

We are mainly concerned with the nonlinear $p$-Laplace equation \begin{equation*} -Δ_pu+ρ|u|^{p-2}u=ψ(x,u) \end{equation*} on a locally finite graph $G=(V,E)$, where $p$ belongs to $(1, +\infty)$. We obtain existence of positive solutions and positive ground state solutions by using the mountain-pass theorem and the Nehari manifold respectively. Moreover, we also analyze the asymptotic behavior for a sequence of positive ground state solutions. Compared with all the existing relevant works, our results have made essential improvements in at least three aspects: $(i)$ $p$ can take any value in $(1, +\infty)$; $(ii)$ the conditions on the graph $G$ and the potential $ρ$ are relaxed; $(iii)$ for the existence of positive solutions, the growth condition in previous works on the nonlinear term $ψ(x,s)$ as $s\rightarrow +\infty$ is removed.

math.AP↗

arXiv2023

Sobolev spaces on locally finite graphs

In this paper, we develop the theory of Sobolev spaces on locally finite graphs, including completeness, reflexivity, separability, and Sobolev inequalities. Since there is no exact concept of dimension on graphs, classical methods that work on Euclidean spaces or Riemannian manifolds can not be directly applied to graphs. To overcome this obstacle, we introduce a new linear space composed of vector-valued functions with variable dimensions, which is highly applicable for this issue on graphs and is uncommon when we consider to apply the standard proofs on Euclidean spaces to Sobolev spaces on graphs. The gradients of functions on graphs happen to fit into such a space and we can get the desired properties of various Sobolev spaces along this line. Moreover, we also derive several Sobolev inequalities under certain assumptions on measures or weights of graphs. As fundamental analytical tools, all these results would be extremely useful for partial differential equations on locally finite graphs.

math.AP↗