# Mining in the Proximity of Subgraphs

@inproceedings{Ketkar2006MiningIT, title={Mining in the Proximity of Subgraphs}, author={Nikhil S. Ketkar and Lawrence B. Holder and Diane Joyce Cook}, year={2006} }

Graphs are a natural way to represent multi-relational data and are extensively used to model a variety of application domains in diverse fields ranging from bioinformatics to homeland security. Often, in such graphs, certain subgraphs are known to possess some distinct properties and graph patterns in the proximity of these subgraphs can be an indicator of these properties. In this work we focus on the task of mining in the proximity of subgraphs, known to possess certain distinct properties… Expand

#### 9 Citations

A new proposal for graph-based image classification using frequent approximate subgraphs

- Mathematics, Computer Science
- Pattern Recognit.
- 2014

This paper proposes a new framework for image classification, which uses frequent approximate subgraph patterns as features and proposes to compute automatically the substitution matrices needed in the process, instead of using expert knowledge. Expand

Frequent approximate subgraphs as features for graph-based image classification

- Computer Science
- Knowl. Based Syst.
- 2012

A new algorithm for mining frequent connected subgraphs over undirected and labeled graph collections VEAM (Vertex and Edge Approximate graph Miner) is presented and a framework for graph-based image classification is introduced. Expand

Scalable SVM-Based Classification in Dynamic Graphs

- Computer Science
- 2014 IEEE International Conference on Data Mining
- 2014

This paper designs an entropy-based sub graph extraction strategy to select informative neighbor nodes and discard those with less discriminative power, to facilitate an effective classification process in large-scale and incrementally changing graphs. Expand

Incremental SVM-based classification in dynamic streaming networks

- Computer Science
- Intell. Data Anal.
- 2016

A framework combining an incremental support vector machine (SVM) with the Weisfeiler-Lehman (W-L) graph kernel is proposed, which design an entropy-based subgraph extraction strategy, that selects informative neighbor nodes and discards those with less discriminative power, to facilitate the classification of nodes in a dynamic network. Expand

A Review on Graph-based Image Classification

- Mathematics
- 2014

Graphs are increasingly important in modelling complicated structures, such as circuits, images, chemical compounds, protein structures, biological networks, social networks and the web. Graph-based… Expand

Scalable classification for large dynamic networks

- Computer Science
- 2015 IEEE International Conference on Big Data (Big Data)
- 2015

An online version of an existing graph kernel is introduced to incrementally compute the kernel matrix for a unbounded stream of extracted subgraphs and a kernel perceptron is adopted to learn a discriminative classifier and predict the class labels of the target nodes with their corresponding sub graphs. Expand

Granular Computing Techniques for Classification and Semantic Characterization of Structured Data

- Computer Science
- Cognitive Computation
- 2015

This paper proposes a system able to synthesize automatically a classification model and a set of interpretable decision rules defined over aSet of symbols, corresponding to frequent substructures of the input dataset, and compares it with two other state-of-the-art graph classifiers, evidencing both its main strengths and limits. Expand

Mission Programming for Deployable Wireless Sensor Networks

- 2006

The fourth year of the PSI project saw a major thrust in the area of Wireless Sensor Networks, Computer and Network Security, Pervasive Computing, Machine Learning, and Databases. Through our… Expand

Finding, extracting and exploiting structure in text and hypertext

- Computer Science
- 2009

Data mining is a fast-developing field of study, using computations to either predict or describe large amounts of data to improve the quality of existing and new data sources. Expand

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