Search arXiv⌕ Search

arXiv · 2610.00107

Theoretical Analysis of DomiRank Centrality: Automorphism, Entropy, and Graph Transformations

Abstract

DomiRank is a node-importance algorithm for unweighted networks, defined by a dynamical-system model whose steady state is governed by a competition-strength parameter, a dominance threshold, and a natural decay rate. We study its intrinsic relations with graph automorphism: vertices mapped to each other by an automorphism share the same DomiRank value, and a graph whose DomiRank values are pairwise distinct must be asymmetric; we derive DomiRank properties of regular and vertex-transitive graphs and bound the number of orbits by that of distinct DomiRank values. For DomiRank entropy, the maximum over connected graphs is attained only by regular graphs, and under sufficient conditions (rigorously in the low-competition regime) the entropy decreases monotonically with the competition parameter, a behavior observed on all tested networks and conjectured to hold generally; tuning sigma shifts the identification from important to dominant key nodes. We also study how graph transformations (vertex similarity, vertex partitions, edge swaps, m-products) affect the DomiRank vector, and characterize analytically the sensitivity and limiting behavior of sigma: the normalized DomiRank distribution is sigma-invariant iff the degree vector is an eigenvector of the adjacency matrix, and sigma interpolates continuously between degree and least-eigenvector centrality; experiments on four real networks confirm these results. These results position DomiRank as a tunable complement to principal-eigenvector centrality, with distinctive behavior under strong competition and new tools for node-importance evaluation. Because the parameterization by sigma is a structural property of the measure, not a guarantee of advantage on a downstream task, we also relate these results to the companion null-model study of how much of DomiRank's task-level edge over a degree baseline survives an explicit degree correction.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yingying Zhang, Chengye Zhao. 2026-10-07. Theoretical Analysis of DomiRank Centrality: Automorphism, Entropy, and Graph Transformations. https://arxiv.org/abs/2610.00107

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Link Fraction Mixed Membership Reveals Community Diversity in Aggregated Social Networks

Community detection is a critical tool for understanding the mesoscopic structure of large-scale networks. However, when applied to aggregated or coarse-grained social networks, disjoint community partitions cannot capture the diverse composition of community memberships within aggregated nodes. While existing mixed membership methods alleviate this issue, they may detect communities that are highly sensitive to the aggregation resolution, not reliably reflecting the community structure of the underlying individual-level network. This paper presents the Link Fraction Mixed Membership (LFMM) method, which computes the mixed memberships of nodes in aggregated networks. Unlike existing mixed membership methods, LFMM is consistent under aggregation. Specifically, we show that it conserves community membership sums at different scales. The method is utilized to study a population-scale social network of the Netherlands, aggregated at different resolutions. Experiments reveal variation in community membership across different geographical regions and evolution over the last decade. In particular, we show how our method identifies large urban hubs that act as the melting pots of diverse, spatially remote communities.

cs.SI↗

Identifying the Group to Intervene on to Maximize Effect Under Cross-Group Interference

In many networked systems, interventions applied to one group of units can induce substantial causal effects on another group through cross-group interference pathways. Despite its practical importance in domains such as public health, digital marketing, and social policy, the problem of identifying which intervention subset in a source group maximizes the benefit on a target group remains largely unaddressed. We formalize this problem as cross-group causal influence estimation and introduce the core-to-group causal effect (Co2G), a formally defined causal estimand that quantifies the contrast in target-group outcomes under intervention versus non-intervention on a candidate source subset. We establish the nonparametric identifiability of Co2G from observational network data using do-calculus under standard causal assumptions, and develop a graph neural network-based estimator that captures cross-group interference patterns. To navigate the combinatorial search space of candidate subsets, we propose CauMax, an uncertainty-aware causal effect maximization framework with two scalable selection algorithms: (i) CauMax-G, an iterative greedy search with Monte Carlo dropout-based lower confidence bounds, and (ii) CauMax-D, a differentiable gradient-based optimization via a concrete Gumbel-Softmax relaxation. Extensive experiments on semi-synthetic data over two real-world social-network topologies, complemented by a real-outcome validation on three additional networks, show that CauMax substantially reduces regret relative to structural and diffusion-based heuristics, by up to an order of magnitude at small budgets and consistently across all budgets, while remaining robust to the uncertainty-penalty weight.

cs.SI↗

Transmission Neural Networks: Inhibitory and Excitatory Connections

This paper extends the Transmission Neural Network model proposed by Gao and Caines in [1]-[3] to incorporate inhibitory connections and neurotransmitter populations. The extended network model contains binary neuronal states, transmission dynamics, and inhibitory and excitatory connections. Under technical assumptions, we establish the characterization of the firing probabilities of neurons, and show that such a characterization considering inhibitions can be equivalently represented by a neural network where each neuron has a continuous state of dimension 2. Moreover, we incorporated neurotransmitter populations into the modeling and establish the limit network model when the number of neurotransmitters at all synaptic connections go to infinity. Finally, sufficient conditions for stability and contraction properties of the limit network model are established.

cs.SI↗