Hanjun Luo, Yingbin Jin, Yiran Wang, Xinfeng Li, Tong Shang, Xuecheng Liu, Ruizhe Chen, Kun Wang, Hanan Salam, Qingsong Wen, Zuozhu Liu. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

The von Neumann graph entropy is a measure of graph complexity based on the Laplacian spectrum. It has recently found applications in various learning tasks driven by the networked data. However, it is computationally demanding and hard to interpret using simple structural patterns. Due to the close relation between the Laplacian spectrum and the degree sequence, we conjecture that the structural information, defined as the Shannon entropy of the normalized degree sequence, might be a good approximation of the von Neumann graph entropy that is both scalable and interpretable. In this work, we thereby study the difference between the structural information and the von Neumann graph entropy named asentropy gap. Based on the knowledge that the degree sequence is majorized by the Laplacian spectrum, we for the first time prove that the entropy gap is between 0 and$\log _{2} e$in any undirected unweighted graphs. Consequently we certify that the structural information is a good approximation of the von Neumann graph entropy that achieves provable accuracy, scalability, and interpretability simultaneously. This approximation is further applied to two entropy-related tasks: network design and graph similarity measure, where a novel graph similarity measure and the corresponding fast algorithms are proposed. Meanwhile, we show empirically and theoretically that maximizing the von Neumann graph entropy can effectively hide the community structure, and then propose an alternative metric calledspectral polarizationto guide the community obfuscation. Our experimental results on graphs of various scales and types show that the very small entropy gap readily applies to a wide range of simple/weighted graphs. As an approximation of the von Neumann graph entropy, the structural information is the only one that achieves both high efficiency and high accuracy among the prominent methods. It is at least two orders of magnitude faster than SLaQ (Tsitsulinet al., 2020) with comparable accuracy. Our structural information based methods also exhibit superior performance in downstream tasks such as entropy-driven network design, graph comparison, and community obfuscation.

While community detection has been one of the cornerstones in network analysis and data science, its opposite, community obfuscation, has received little attention in recent years. With the increasing awareness of data security and privacy protection, the need to understand the impact of such attacks on traditional community detection algorithms emerges. To this end, we investigate the community obfuscation problem which aims to hide a target set of communities from being detected by perturbing the network structure. We identify and analyze the Matthew effect incurred by the classical quality function based methods, which essentially results in the imbalanced allocation of perturbation resources. To mitigate such effect, we propose a probabilistic framework named as ProHiCo to hide communities. The key idea of ProHiCo is to first allocate the resource of perturbations randomly and fairly and then choose the appropriate edges to perturb via likelihood minimization. Our ProHiCo framework provides the additional freedom to choose the generative graph model with community structure. By incorporating the stochastic block model and its degree-corrected variant into the ProHiCo framework, we develop two scalable and effective algorithms called SBM and DCSBM. Via extensive experiments on 8 real-world networks and 5 community detection algorithms, we show that both SBM and DCSBM are about 30x faster than the prominent baselines in the literature when there are around 500 target communities, while their performance is comparable to the baselines.

The von Neumann graph entropy (VNGE) is a measure of graph complexity based on the Laplacian spectrum. It has recently found applications in various learning tasks driven by networked data. However, it is computationally demanding and hard to interpret using simple structural patterns. Due to the close relation between Lapalcian spectrum and degree sequence, we conjecture that the structural information, defined as the Shannon entropy of the normalized degree sequence, might approximate VNGE well.

Wireless power transfer (WPT) is the preferred charging method for battery-powered electric vehicles (EVs). In this paper, a novel WPT system with constant current charging capability is introduced for warehouse vehicles with lead-acid batteries. First, the schematic structure of the proposed system is discussed. A novel structure of asymmetrical loosely coupled transformer is designed to enable high-voltage feeding to low-voltage output and reduce the weight of the secondary side. Secondly, an optimized magnetic coupler using ferrite cores and magnetic shielding structure is proposed to ensure stable power transmission and high efficiency. Then, a dual-side independent control strategy on dual active bridge is proposed, aiming for maintaining constant charging current and high system efficiency. Finally, a 3kW inductive charging experimental platform is established to validate the theoretical analysis. Constant charging current of 45 A is achieved at the transmission distance of 50mm. The rated transferring power and system efficiency are 3 kW and 94.3%, respectively.

We study the source detection problem using limited timestamps on a given network. Due to the NP-completeness of the maximum likelihood estimator (MLE), we propose an approximation solution called infection-path-based estimator (INF), the essence of which is to identify the most likely infection path that is consistent with observed timestamps. The source node associated with that infection path is viewed as the estimated source û. For the tree network, we transform the INF into integer linear programming and find a reduced search region using BFS, within which the estimated source is provably always on a path termed as candidate path. This notion enables us to analyze the accuracy of the INF in terms of error distance on arbitrary tree. Specifically, on the infinite g-regular tree with uniform sampled timestamps, we get a refined performance guarantee in the sense of a constant bounded d(u*, û). By virtue of time labeled BFS tree, the estimator still performs fairly well when extended to more general graphs. Simulations on both trees and general networks further demonstrate the superior performance of the INF.

Motivated by the requirement of heterogeneity in the Internet of Things, we initiate the joint study of capacity and energy efficiency scaling laws in heterogeneous wireless sensor networks, and so on. The whole network is composed of n nodes scattered in a square region with side length L = n α , and there are m = n ν home points { c j } j=1 m , where a generic home point c j generates q j nodes independently according to a stationary and rotationally invariant kernel k ( c j , ⋅). Among the n nodes, we schedule n s independent multicast sessions each consisting of k − 1 destination nodes and one source node. According to the heterogeneity of nodes’ distribution, we classify the network into two regimes: a cluster-dense regime and a cluster-sparse regime. For the cluster-dense regime, we construct single layer highway system using percolation theory and then build the multicast spanning tree for each multicast session. This scheme yields the Ω( n ½+(α − ½)γ / n s √ k ) per-session multicast capacity. For the cluster-sparse regime, we partition the whole network plane into several layers and construct nested highway systems. The similar multicast spanning tree yields the Ω( n ½−(1− ν)γ/2 / n s √ k ) per-session multicast capacity, where γ is the power attenuation factor. Interestingly, we find that the bottleneck of multicast capacity attributes to the network region with largest node density, which provides a guideline for the deployment of sensor nodes in large-scale sensor networks. We further analyze the upper bound of multicast capacity and the per-session multicast energy efficiency. Using both synthetic networks and real-world networks (i.e., Greenorbs), we evaluate the asymptotic capacity and energy efficiency and find that the theoretical scaling laws are gracefully supported by the simulation results. To our best knowledge, this is the first work verifying the scaling laws using real-world large-scale sensor network data.

ABSTRACT Large‐scale group performance animation has been an important research topic because of its diverse range of applications including virtual rehearsal and film production. Animating hundreds of virtual actors as what the director wishes is a tough task. In this paper, we address this challenge by introducing an optimization method that generates large‐scale group performance by deducing a small‐scale one with fewer actors. We introduced group motion bigraph technique and transformed the motion‐deducing problem into a constrained optimization problem. A solving process is then presented to automatically obtain the motion of the large group with velocity constraints. Moreover, an interactive system of constructing the group motion bigraph has been implemented, which provides flexible edit and control on deducing group motion. The animation results show that our method is competent for deducing large‐scale group performance from only several motion clips performed by small groups. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract Human face is a complex biomechanical system and non‐linearity is a remarkable feature of facial expressions. However, in blendshape animation, facial expression space is linearized by regarding linear relationship between blending weights and deformed face geometry. This results in the loss of reality in facial animation. To synthesize more realistic facial animation, aforementioned relationship should be non‐linear to allow the greatest generality and fidelity of facial expressions. Unfortunately, few existing works pay attention to the topic about how to measure the non‐linear relationship. In this paper, we propose an optimization scheme that automatically explores the non‐linear relationship of blendshape facial animation from captured facial expressions. Experiments show that the explored non‐linear relationship is consistent with the non‐linearity of facial expressions soundly and is able to synthesize more realistic facial animation than the linear one.

Abstract This paper presents a labor‐saving method to construct optimal facial animation blendshapes from given blendshape sketches and facial motion capture data. At first, a mapping function is established between target “Marker Face” and performer's face by RBF interpolating selected feature points. Sketched blendshapes are transferred to performer's “Marker Face” by using motion vector adjustment technique. Then, the blendshapes of performer's “Marker Face” are optimized according to the facial motion capture data. At last, the optimized blendshapes are inversely transferred to target facial model. Apart from that, this paper also proposes a method of computing blendshape weights from facial motion capture data more accurately. Experiments show that expressive facial animation can be acquired. Copyright © 2008 John Wiley & Sons, Ltd.