Graph Mining Framework for Finding and Visualizing Substructures Using Graph Database

Shrivastava, Swapnil; Pal, Supriya N.

doi:10.1109/asonam.2009.16

Cited by 8 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some proposals are focused on how to model realistic graphs that match the patterns found in real-world graphs. Shrivastava et al [25] proposed a graph mining framework that captures entities and relations between entities from different data sources. Their framework offers a comprehensive approach with five modules covering graph preprocessing, graph database, dense substructure extraction, frequent substructure discovery, and graph visualization.…”

Section: Framework and Methodologiesmentioning

confidence: 99%

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

Ortega-Guzmán,

Gutiérrez-Preciado,

Cervantes

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Graph mining has emerged as a significant field of research with applications spanning multiple domains, including marketing, corruption analysis, business, and politics. The exploration of knowledge within graphs has garnered considerable attention due to the exponential growth of graph-modeled data and its potential in applications where data relationships are a crucial component, and potentially being even more important than the data themselves. However, the increasing use of graphs for data storing and modeling presents unique challenges that have prompted advancements in graph mining algorithms, data modeling and storage, query languages for graph databases, and data visualization techniques. Despite there being various methodologies for data analysis, they predominantly focus on structured data and may not be optimally suited for highly connected data. Accordingly, this work introduces a novel methodology specifically tailored for knowledge discovery in labeled and heterogeneous graphs (KDG), and it presents three case studies demonstrating its successful application in addressing various challenges across different application domains.

show abstract

Section: Framework and Methodologiesmentioning

confidence: 99%

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

Ortega-Guzmán,

Gutiérrez-Preciado,

Cervantes

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Indeed, when objects are correlated with the nodes of a graph and their relationships are associated with the edges, it is possible to add data from different sources into one structure effectively. The graph structure is the most convenient for representing complex engineering information when we need to constantly develop and maintain the data model throughout its life cycle [27]. Information in a semantic form is easily rebuilt and expanded when new sources become available, without the need for primary processing of the storage system, as in the case of traditional databases.…”

Section: Problem Set-upmentioning

confidence: 99%

Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology

Yerashenia

Bolotov

2019

2019 IEEE 21st Conference on Business Informatics (CBI)

View full text Add to dashboard Cite

In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company's financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database system.The Ontological layer defines the basic concepts of the financial risk management as well as the objects that serve as sources of knowledge for predicting a company's bankruptcy. The Graph Database layer utilises a powerful semantic data technology, which serves as a semantic data repository for our model.The article provides a detailed description of the construction of the Ontology and its informal conceptual representation. We also present a working prototype of the Graph Database system, constructed using the Neo4j application, and show the connection between well-known financial ratios.We argue that this methodology which utilises state of the art semantic data management mechanisms enables data processing and relevant computations in a more efficient way than approaches using the traditional relational database. These give us solid grounds to build a system that is capable of tackling the data of any complexity level.

show abstract

“…are all read along with the topological structure of the network and interaction history over time. A unified graph mining framework (Shrivastava & Pal, 2009) is presented that depicts entities, relationship between entities, dense sub-structure or cluster detection, discovery of frequent substructures, informative and interactive visualization of the mined knowledge from the graph representation. The proposed framework in (Shrivastava & Pal, 2009) is broadly classified into five modules based on its functionalities.…”

Section: Introductionmentioning

confidence: 99%

“…The challenges that this model (Shrivastava & Pal, 2009) has taken is to make the graph mining tool scalable, efficient and robust for mining. Also, the visualization tool should be interactive enough for easy readability.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework for Friend Recommender System based on Graph Theoretic Approach

Ganguli

Mehta²,

Debnath

et al.

EPiC Series in Computing

View full text Add to dashboard Cite

Study of relationships established in social media is an emerging area of research. Online Social Network (OSN) is a collection of social entities carrying a lot of information that enriches the network. A structured modelling of the OSN dataset is required for informative knowledge mining and efficient Social Network Analysis (SNA). Graphical representation of data helps in analysing the structural properties, study of dense substructure, cluster formation and identifying the numerous types of entities exhibiting associations based on different activity fields. This paper discusses about various ways of graph theoretic representations of OSN including structure-based and content or interaction-based approaches. An integrated framework is proposed in this paper that learns from various user attributes and its associated interactions, network structure, timeline history, etc from a polarized OSN Graph for generating an efficient Friend Suggestion Recommender System.

show abstract

Graph Mining Framework for Finding and Visualizing Substructures Using Graph Database

Cited by 8 publications

References 3 publications

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology

An Integrated Framework for Friend Recommender System based on Graph Theoretic Approach

Contact Info

Product

Resources

About