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**1 - 6**of**6**### Edited by:

, 2013

"... lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers ca ..."

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lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models.

### AN TEXT CLASSIFICATION APPROACH BASED ON THE GRAPH SPACE MODEL

"... To do the text classification on the basis of VSM, and use the maximum common subgraph to measure two graphs ’ similarities are the relatively common methods, but these methods have not made full use of lots of semantic information spatial model contained, so the text classification performance is g ..."

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To do the text classification on the basis of VSM, and use the maximum common subgraph to measure two graphs ’ similarities are the relatively common methods, but these methods have not made full use of lots of semantic information spatial model contained, so the text classification performance is generally poor. In order to improve the classification results of the graph, on the basis of the structural equivalence, this paper further analyzes the maximum common substructure graph nodes and edges if it is a true semantic equivalence, and puts forward a kind of improvement text similarity metrics based on the graph space model. Then apply it to the text classification, the classification performance has been improved. Finally, verify the effectiveness of this method by experiment.

### Chapter 1

"... 1.2 Basic concepts............................................................. 2 ..."

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1.2 Basic concepts............................................................. 2

### Mining Closed Patterns in Relational, Graph and Network Data

"... Recent theoretical insights have led to the introduction of efficient algorithms for mining closed item-sets. This paper investigates potential generalizations of this paradigm to mine closed patterns in relational, graph and network databases. Several semantics and associated definitions for closed ..."

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Recent theoretical insights have led to the introduction of efficient algorithms for mining closed item-sets. This paper investigates potential generalizations of this paradigm to mine closed patterns in relational, graph and network databases. Several semantics and associated definitions for closed patterns in relational data have been introduced in previous work, but the differences among these and the implications of the choice of semantics was not clear. The paper investigates these implications in the context of generalizing the LCM algorithm, an algorithm for enumerating closed item-sets. LCM is attractive since its run time is linear in the number of closed patterns and since it does not need to store the patterns output in order to avoid duplicates, further reducing memory signature and run time. Our investigation shows that the choice of semantics has a dramatic effect on the properties of closed patterns and as a result, in some settings a generalization of the LCM algorithm is not possible. On the other hand, we provide a full generalization of LCM for the semantic setting that has been previously used by the Claudien system. 1

### Leveraging Graph Dimensions in Online Graph Search

"... ABSTRACT Graphs have been widely used due to its expressive power to model complicated relationships. However, given a graph database DG = {g1, g2, · · · , gn}, it is challenging to process graph queries since a basic graph query usually involves costly graph operations such as maximum common subgr ..."

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ABSTRACT Graphs have been widely used due to its expressive power to model complicated relationships. However, given a graph database DG = {g1, g2, · · · , gn}, it is challenging to process graph queries since a basic graph query usually involves costly graph operations such as maximum common subgraph and graph edit distance computation, which are NP-hard. In this paper, we study a novel DSpreserved mapping which maps graphs in a graph database DG onto a multidimensional space MG under a structural dimension M using a mapping function φ(). The DS-preserved mapping preserves two things: distance and structure. By the distance-preserving, it means that any two graphs gi and gj in DG must map to two data objects φ(gi) and φ(gj) in MG, such that the distance, d(φ(gi), φ(gj)), between φ(gi) and φ(gj) in MG approximates the graph dissimilarity δ(gi, gj) in DG. By the structure-preserving, it further means that for a given unseen query graph q, the distance between q and any graph gi in DG needs to be preserved such that δ(q, gi) ≈ d(φ(q), φ(gi)). We discuss the rationality of using graph dimension M for online graph processing, and show how to identify a small set of subgraphs to form M efficiently. We propose an iterative algorithm DSPM to compute the graph dimension, and discuss its optimization techniques. We also give an approximate algorithm DSPMap in order to handle a large graph database. We conduct extensive performance studies on both real and synthetic datasets to evaluate the top-k similarity query which is to find top-k similar graphs from DG for a query graph, and show the effectiveness and efficiency of our approaches.

### Edited by:

, 2013

"... lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers ca ..."

Abstract
- Add to MetaCart

lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models.