On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Graph distances using graph union
Pattern Recognition Letters
Conceptual Knowledge Discovery and Data Analysis
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Greedy approximation algorithms for finding dense components in a graph
APPROX '00 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Frequent subgraph mining in outerplanar graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparison of descriptor spaces for chemical compound retrieval and classification
Knowledge and Information Systems
Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Partial least squares regression for graph mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimizing Feature Sets for Structured Data
ECML '07 Proceedings of the 18th European conference on Machine Learning
Understanding Social Networks Using Formal Concept Analysis
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Counting Subgraphs via Homomorphisms
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Formal concept analysis in information science
Annual Review of Information Science and Technology
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Fast, effective molecular feature mining by local optimization
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Formal concept analysis based clustering for blog network visualization
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Effective feature construction by maximum common subgraph sampling
Machine Learning
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
A New Adaptive Structural Signature for Symbol Recognition by Using a Galois Lattice as a Classifier
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 12.05 |
Graph data have been of common practice in many application domains. However, it is very difficult to deal with graphs due to their intrinsic complex structure. In this paper, we propose to apply Formal Concept Analysis (FCA) to learning from graph data. We use subgraphs appearing in each of graph data as its attributes and construct a lattice based on FCA to organize subgraph attributes which are too numerous. For statistical learning purpose, we propose a similarity measure based on the concept lattice, taking into account the lattice structure explicitly. We prove that, the upper part of the lattice can provide a reliable and feasible way to compute the similarity between graphs. We also show that the similarity measure is rich enough to include some other measures as subparts. We apply the measure to a transductive learning algorithm for graph classification to prove its efficiency and effectiveness in practice. The high accuracy and low running time results confirm empirically the merit of the similarity measure based on the lattice.