A new centrality metric called Node Pack Fuzzy Information Centrality (NPFIC), which suggests that crucial information about a node's significance can be derived from the internal structure of its pack and is compared with four established centrality measures.
The Influence-Spreading Model (ISM) introduces three probabilistic centrality measures: out-centrality, in-centrality, and ISM betweenness centrality. Out-centrality measures the average probability that a node influences others, while in-centrality measures the average probability that others influence a node. ISM betweenness centrality measures the change in total probabilistic influence when a node is removed. These measures depend on edge transmission probabilities and allow walks up to a specified maximum length. We compare the ISM centrality measures to commonly used weighted variants of out-degree, in-degree, closeness, shortest-path betweenness, and Katz centrality in directed, weighted networks using four real-world online social networks and nine synthetic networks generated by Erd\H{o}s-R\'enyi, navigable small-world, and directed scale-free models. For the synthetic networks, the edge probabilities are drawn from three beta distributions. We evaluate the similarity in centrality values and their ranking using Pearson correlation and Spearman's rank correlation coefficients. Results show strong correlations between the ISM out-centrality and weighted out-degree and outward Katz centrality, particularly for low edge probabilities. Conversely, relationships between the ISM in-centrality and other measures vary with network topology, sometimes yielding negative correlations. Correlations between the ISM betweenness and the shortest-path betweenness are also topology-dependent and weaken as alternative influence paths become more relevant. Overall, standard centrality measures can approximate the influence of broadcasting influence but often miss the nuances of receiving influence and probabilistic intermediary roles.
This study introduces a new ranking framework that integrates a quasi-Laplacian structural measure with a gravity-inspired aggregation process and demonstrates that the proposed framework consistently outperforms existing techniques in terms of accuracy, resolution, and computational simplicity.
Centrality measures are defined for simple graphs -- directed, undirected, weighted or unweighted. Attributed graphs have to be reduced to simple graphs for computing centrality measures. However, when applications with multiple types of relationships are modeled using multilayer networks (MLNs), simple graph algorithms cannot be directly used. Existing approaches typically analyze MLNs by aggregating layers of an MLN into a single graph, which results in the loss of structural and semantic information. The semantic information loss can be more pronounced particularly, in weighted networks. This work focuses on computing degree centrality in weighted homogeneous multilayer networks (HoMLNs) using a decoupling-based framework. The framework performs independent layer-wise analysis on MLNs without reducing them to simple graphs. The decoupling approach allows use of exiting algorithms for each layer and uses minimal information from individual layers for computing degree centrality of HoMLNs. We propose heuristic-based algorithms that strike a balance between accuracy and efficiency. The proposed methods are evaluated against ground truth (GT) results obtained using Boolean OR aggregation and naive baselines. Experimental results on both synthetic and real-world HoMLN datasets demonstrate that the heuristics achieve accuracy comparable to the ground truth while significantly improving computational efficiency, thereby establishing the scalability and effectiveness of the HoMLN algorithms developed using the decoupling approach.
A. Ayowole-Obi, Abhishek Santra, Sharma Chakravarthy· 0 citations
When analyzing networks, Betweenness Centrality is a method of measurement that can be used to find the frequency at which a node is accessed when calculating the shortest path between various nodes. The more often a node is accessed, the greater the node’s Betweenness Centrality score becomes. This metric allows for numerous practical applications, from gene identification for diseases to analyzing the movement of data through informational infrastructures. With modern day computing power, we are able to utilize LLMs (Large Language Models) to calculate Betweenness Centrality on large-scale networks. However, the performance of LLMs may not be quick nor accurate when presenting a response for Betweenness Centrality graph problems. Therefore, we propose a GPS-BC module that has the capability to formulate accurate solutions for Betweenness Centrality problems using tailored algorithms. The problems contain several combinations of the constraints on (A) node color, (B) edge/path weights and (C) node degree. For all the constrained problems, GPT-5.2 was observed to have an average accuracy of 62% when reporting Betweenness Centrality scores. The accuracy decreases when increasing the complexity of the constraints imposed on a problem. Additionally, GPSBC algorithms were found to be, on average, 3.65× faster than the observed processing time for GPT-5.2. Through this approach of using validated algorithms in our GPS-BC module, we are able to process Betweenness Centrality calculations with efficiency and ensured accuracy.
Terry Chang, Chung-Hsien Chou· International Journal of Sem...· 0 citations
Experimental results in seven complex networks of the real world show that the proposed hybrid centrality called k-core neighborhood density (KND) remarkably balances rank distribution and accuracy, and the identified influential nodes have a superior ability to spread their influence over a wide area of a network.
WKDH fuses local structural attributes with global structural attributes via a multiplicative weighted synergy model, simultaneously capturing local connection “quantity,” local connection “quality,” and global core-layer position, and achieves linear computational complexity of O(m).
Na Zhao, Chaozhou Dai, Guo-Lin Yang et al.· Entropy· 0 citations