Results 91 to 100 of about 11,781 (210)

Quantifying Structural Complexity, Effort, and Performance: An Early Experiment Using Network Design Tasks

Systems Engineering, EarlyView.
ABSTRACT Structural Complexity is perceived as driving cost in system development, yet managing it effectively requires empirical understanding. This study investigates human decision‐making using a toy transportation‐style network design task, focusing on how Structural Complexity, Effort, and Performance interact. Seventy‐four participants (primarily
Alfonso Lanza, Joshua Sutherland, Marc‐Andre Chavy‐Macdonald, Olivier L. de Weck   +3 more
wiley   +1 more source

Distributed Subgraph Matching on Big Knowledge Graphs Using Pregel

IEEE Access, 2019
With RDF becoming the de facto standard for representing knowledge graphs, it is indispensable to develop scalable subgraph matching algorithms over big RDF graphs stored in distributed clusters.
Qiang Xu, Xin Wang, Jianxin Li, Qingpeng Zhang, Lele Chai   +4 more
doaj   +1 more source

Assessing Capability Complexity Using Enterprise Architecture Framework

Systems Engineering, EarlyView.
ABSTRACT This study proposes a structured and quantitative methodology to evaluate the holistic complexity of system‐of‐systems (SoSs), employing the Zachman Architecture Framework (ZAF) as its foundational analytical tool. A five‐phase analytical procedure is developed and empirically validated, encompassing: (1) refinement of complexity measures, (2)
Javad Bakhshi, Mahmoud Efatmaneshnik
wiley   +1 more source

A Bridge Transformer Network With Deep Graph Convolution for Hyperspectral Image Classification

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Transformers have been widely applied to hyperspectral image classification, leveraging their self‐attention mechanism for powerful global modelling. However, two key challenges remain as follows: excessive memory and computational costs from calculating correlations between all tokens (especially as image size or spectral bands increase) and ...
Yuquan Gan, Siyu Wu, Chang Su, Nan Xiang, Zhijie Xu, Yushan Pan   +5 more
wiley   +1 more source

HPoolGCL: Augmentation‐Free Cross‐Granularity Graph Contrastive Learning With Hierarchical Pooling

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Graph contrastive learning (GCL) has emerged as a dominant paradigm for self‐supervised representation learning for attributed graph data. However, existing GCL methods heavily rely on empirical graph data augmentation, which may distort intrinsic graph semantics and produce poor generalisation without carefully chosen or designed augmentation
Fenglin Cen, Shijie Liu, Quan Feng, Tiantian He, Ji Xu   +4 more
wiley   +1 more source

Generating Compressed Counterfactual Hard Negative Samples for Graph Contrastive Learning

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Graph contrastive learning (GCL) relies on acquiring high‐quality positive and negative samples to learn the structural semantics of the input graph. Previous approaches typically sampled negative samples from the same training batch or an irrelevant external graph.
Haoran Yang, Hongxu Chen, Xiangyu Zhao, Sixiao Zhang, Xiangguo Sun, Qian Li, Hongzhi Yin, Guandong Xu   +7 more
wiley   +1 more source

A tutorial on Bayesian model averaging for exponential random graph models

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract The use of exponential random graph models (ERGMs) is becoming prevalent in psychology due to their ability to explain and predict the formation of edges between vertices in a network. Valid inference with ERGMs requires correctly specifying endogenous and exogenous effects as network statistics, guided by theory, to represent the network ...
Ihnwhi Heo, Jan‐Willem Simons, Haiyan Liu   +2 more
wiley   +1 more source

Pairwise Imitation and Tournament Graphs

International Economic Review, EarlyView.
ABSTRACT This paper investigates strategic dynamics under the behavioral rule of pairwise interact and imitate (PII), which requires minimal information and emphasizes outperforming opponents in pairwise interactions. We characterize PII using weak tournament graphs and, for a broad class of dynamics, establish a one‐shot stability result for ...
Sung‐Ha Hwang, Philip R. Neary, Jonathan Newton, Ryoji Sawa   +3 more
wiley   +1 more source

Accelerating Subgraph Matching Through Advanced Compression and Label Filtering

Algorithms
Efficiently identifying subgraphs that match a given query graph within large-scale graphs has become a critical focus in both academic and industrial research.
Yanfeng Chai, Jiashu Li, Qiang Zhang
doaj   +1 more source

Subgraph Covers: An Information-Theoretic Approach to Motif Analysis in Networks

Physical Review X, 2014
Many real-world networks contain a statistically surprising number of certain subgraphs, called network motifs. In the prevalent approach to motif analysis, network motifs are detected by comparing subgraph frequencies in the original network with a ...
Anatol E. Wegner
doaj   +1 more source