Results 161 to 170 of about 27,298 (312)

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

CP models for maximum common subgraph problems

, 2011
. The distance between two graphs is usually defined by means of the size of a largest common subgraph. This common subgraph may be an induced subgraph, obtained by removing nodes, or a partial subgraph, obtained by removing arcs and nodes. In this paper,
Solnon, Christine, Samba Ndojh Ndiaye, Christine Solnon, Ndiaye, Samba Ndojh   +3 more
core   +1 more source

A Novel Full‐Chain Loop Tracking Auditing Framework for Obtaining Audit Evidence With Reasonable Assurance

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT As an attestation engagement, auditing is required to provide reasonable assurance for its conclusions. Traditional auditing has limited capacity to handle unstructured data and is usually based on audit sampling techniques, which can lead to the neglect of important audit evidence during the auditing process and result in a higher audit risk,
Xiaojia Wang, Ziqing Luo, Chaoxu Mu
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

Terrain Synthesis and Authoring based on Iso‐Contours

Computer Graphics Forum, EarlyView.
Abstract Digital terrains are central to realistic landscape depiction, yet authoring tools must balance perceptual realism with intuitive artistic control. We propose a compact vector‐based representation that models terrain as nested iso‐contours, inspired by geomorphology and cartography.
B. Huftier, H. Schott, E. Galin, O. Argudo, A. Peytavie, E. Guérin   +5 more
wiley   +1 more source

Subgraph random sampling pseudocode.

, 2016
Subgraph random sampling pseudocode.
Michael Gribskov (11227), Kejie Li (413169), Jiajie Huang (3226605)   +2 more
core   +1 more source

Noisy Graph Patterns via Ordered Matrices

Computer Graphics Forum, EarlyView.
Abstract The high‐level structure of a graph is a crucial ingredient for the analysis and visualization of relational data. However, discovering the salient graph patterns that form this structure is notoriously difficult for two reasons. (1) Finding important patterns, such as cliques and bicliques, is computationally hard.
J. Wulms, W. Meulemans, B. Speckmann
wiley   +1 more source

Full duplicate candidate pruning for frequent connected subgraph mining

, 2010
Support calculation and duplicate detection are the most challenging and unavoidable subtasks in frequent connected subgraph (FCS) mining. The most successful FCS mining algorithms have focused on optimizing these subtasks since the existing solutions ...
JESUS ARIEL CARRASCO OCHOA, JOSE FRANCISCO MARTINEZ TRINIDAD, ANDRÉS GAGO ALONSO   +2 more
core  

A semi-induced subgraph characterization of upper domination perfect graphs [PDF]

, 1999
Let β(G) and Γ(G) be the independence number and the upper domination number of a graph G, respectively. A graph G is called Γ-perfect if β(H) = Γ(H), for every induced subgraph H of G. The class of Γ-perfect graphs generalizes such well-known classes of
Zverovich, Vadim, Zverovich, Igor E., Zverovich, Igor, Zverovich, Vadim E.   +3 more
core  

Quantitative Metrics for Edge Bundling of Network Visualizations

Computer Graphics Forum, EarlyView.
Abstract Edge bundling is widely used for reducing visual clutter in large 2D network and trajectory visualizations. Various edge bundling methods have been proposed, each producing qualitatively distinct outputs for the same data; however, few quantitative metrics exist for systematic evaluation. In this paper, we propose a set of quantitative metrics
M. Wallinger, J. Miller, A. Maftei, S. G. Kobourov   +3 more
wiley   +1 more source