Plagiarism Detection Using Graph-Based Representation

TL;DR

This paper introduces a novel graph-based method for detecting plagiarism by representing documents as graphs with topic signatures, aiming to improve effectiveness and efficiency over traditional fingerprint methods.

Contribution

The paper proposes a new graph representation technique incorporating topic signatures for more accurate and efficient plagiarism detection.

Findings

Graph-based representation effectively captures document structure.

Topic signatures provide quick guidance for comparison.

Method shows potential for improved detection performance.

Abstract

Plagiarism of material from the Internet is a widespread and growing problem. Several methods used to detect the plagiarism and similarity between the source document and suspected documents such as fingerprint based on character or n-gram. In this paper, we discussed a new method to detect the plagiarism based on graph representation; however, Preprocessing for each document is required such as breaking down the document into its constituent sentences. Segmentation of each sentence into separated terms and stop word removal. We build the graph by grouping each sentence terms in one node, the resulted nodes are connected to each other based on order of sentence within the document, all nodes in graph are also connected to top level node "Topic Signature". Topic signature node is formed by extracting the concepts of each sentence terms and grouping them in such node. The main advantage of the proposed method is the topic signature which is main entry for the graph is used as quick guide to the relevant nodes. which should be considered for the comparison between source documents and suspected one. We believe the proposed method can achieve a good performance in terms of effectiveness and efficiency.