This essay deals with a graph search for communities with corresponding keywords.
The era of big data and world-spanning social networks has highlighted the necessity of ways to make sense of this vast amount of information. Data can be arranged in a graph of connected vertices, therefore giving it a basic structure. If the vertices are further described by keywords, the structure is called an attributed graph. This paper discusses a query algorithm that scans these attributed graphs for communities that are not only structurally linked - therefore forming subgraphs - but also share the same keywords. This method might give new insights into the composition of large networks, highlight interesting connections and give opportunities for effectively targeted marketing. As a specific use case, the idea of the attributed community query is applied to the example of a film recommendation program.
Inhaltsverzeichnis (Table of Contents)
- INTRODUCTION
- ATTRIBUTED COMMUNITY GRAPHS
- RELATED WORK
- A new challenge in graph clustering
- Community detection vs. Community search
- EFFECTIVE COMMUNITY SEARCH FOR LARGE ATTRIBUTED GRAPHS
- Problem definition
- Implementation
- The core label tree
- Query algorithms
- Experiments
- Setup and structure
- Advantages and Drawbacks of the ACQ algorithm
- A POTENTIAL USE CASE: A FILM RECOMMENDATION SYSTEM
- CONCLUSION
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This paper explores the development of a community search algorithm for attributed graphs, aiming to provide a method for efficient and effective retrieval of communities within large-scale networks. The algorithm focuses on combining structural and attributive information to achieve a deeper understanding of network structures and relationships.
- Attributed graphs and community structure
- Challenges in graph clustering and community detection
- Community search algorithms for attributed graphs
- The core label tree index structure for efficient query processing
- Potential applications of the algorithm in various domains
Zusammenfassung der Kapitel (Chapter Summaries)
- INTRODUCTION: Introduces the concept of attributed graphs and their relevance in the era of big data and social networks. Highlights the need for efficient methods to analyze and understand these networks.
- ATTRIBUTED COMMUNITY GRAPHS: Defines the concept of attributed graphs, emphasizing the structural and thematic dimensions. Introduces the notion of attributed communities (AC) and their characteristics.
- RELATED WORK: Reviews existing research on graph clustering, community detection, and community search. Discusses the limitations of traditional approaches and the need for algorithms that incorporate both structural and attributive information.
- EFFECTIVE COMMUNITY SEARCH FOR LARGE ATTRIBUTED GRAPHS: Presents the ACQ algorithm developed by Fang et al., focusing on its problem definition, implementation details, and experimental evaluation. Describes the core label tree index structure and its role in efficient query processing.
- A POTENTIAL USE CASE: A FILM RECOMMENDATION SYSTEM: Explores the applicability of the ACQ algorithm in a film recommendation system, highlighting the potential for personalized recommendations based on user preferences and movie attributes.
Schlüsselwörter (Keywords)
The main focus of this paper lies in the development and evaluation of an algorithm for attributed community queries. This involves exploring the concepts of attributed graphs, community structure, graph clustering, community detection, and community search. The algorithm leverages the core label tree index structure for efficient query processing and provides a promising solution for analyzing large-scale attributed networks in various domains.
Frequently Asked Questions
What is an attributed graph in data science?
An attributed graph is a structure where vertices (nodes) are not only connected by edges but also described by specific keywords or attributes.
What is the purpose of the ACQ algorithm?
The Attributed Community Query (ACQ) algorithm scans large networks for communities that share both structural links and common keywords.
How can community search be used in marketing?
It allows for effectively targeted marketing by identifying groups of people with similar interests and strong social connections.
What is a core label tree?
A core label tree is an index structure used by the ACQ algorithm to make the search process in large attributed graphs more efficient.
Can this method be applied to film recommendations?
Yes, it can identify communities of users with similar movie tastes (attributes) and recommend films based on the preferences of that specific cluster.
- Quote paper
- Andrea Attwenger (Author), 2017, Social Networks and Questions of Big Data. Graph search for communities with corresponding keywords, Munich, GRIN Verlag, https://www.grin.com/document/369515
