A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence properties of the softassign quadratic assignment algorithm
Neural Computation
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Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A New Algorithm for Inexact Graph Matching
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Graph-based technologies for intelligence analysis
Communications of the ACM - Homeland security
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining in an engineering design environment: OR applications from graph matching
Computers and Operations Research
Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems
Computers and Operations Research
On comparing bills of materials: a similarity/distance measure for unordered trees
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
NeMa: fast graph search with label similarity
Proceedings of the VLDB Endowment
Discovering patterns in social networks with graph matching algorithms
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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The intent of this paper is to show enhancements in Levels 2 and 3 fusion capabilities through a new class of models and algorithms in graph matching. The problem today is not often lack of data, but instead, lack of information and data overload. Graph matching algorithms help us solve this problem by identifying meaningful patterns in voluminous amounts of data to provide information. In this paper we investigate a classical graph matching technique for subgraph isomorphism. A complete implementation of a heuristic approach (since the problem under consideration is NP-Hard) using an inexact isomorphism technique has been used. The heuristic approach is called Truncated Search Tree algorithm (TruST), where the state space of the problem is constrained using breadth and depth control parameters. The breadth and depth control parameters are then studied using design of experiment based inferential statistics. Finally, a software implementation of the procedure has been completed.