Due to the vast availability of texts and documents through the Internet (web pages, data warehouses, digital documents, scanned or transcribed, etc.) has become increasingly effortless to retrieve ideas. Unluckily, this phenomenon becomes proportional to the upsurge in cases of plagiarism. Undoubtedly, appropriating content without the permission of its author or citing its references is regarded as a case of plagiarism. Furthermore, plagiarism is becoming a profound issue in many areas, and research shows that paraphrasing, especially in the cross-lingual cases of reuse, is much harder to detect. Moreover, the recent rise in readily available multilingual content on the Web and social media has increased the problem to an unprecedented scale. The main objective of this thesis was to explore several facets of plagiarism detection by advancing the field on several facades. We offered a cascade of approaches for detecting some types of plagiarism while producing high-quality results. We particularly limit ourselves to paraphrastic, translation and plagiarism of ideas. We do not focus on literal forms of plagiarism, such as copying and pasting. Instead, we worked on the scale of the document and smaller passages like citations and sentences. We tackled in this thesis and advanced state of the art on multiple types of plagiarism detection, namely on extrinsic, intrinsic and cross-lingual plagiarism detection. For the extrinsic plagiarism detection first sub-task, which is plagiarism candidate retrieval , we developed a new document-level representation that effectively represents the global and local semantic and syntactic information. The results proved that our methods compete with the state of the art models on this sub-task. We propose a citation-based pairwise document similarity based on the best-performed word and sentence embedding regarding the second sub-task. Moreover, we decided to tackle the intrinsic plagiarism detection task by proposing a new style-embedding based on the attention mechanism. Our evaluation of this approach shows processing results on detecting style changes and breaches. Our final contribution is on the cross-lingual plagiarism analysis task, where we implement and develop a new model called Cross-Lingual Graph Transformer based Analysis (CL-GTA). We introduced Graph Transformer, a new graph representation method based on the transformer architecture that uses explicit relation encoding and provides a more efficient way to represent global graph dependencies. The experimental results show that using the Graph Transformer mechanism provided our model with state-of-the-art performance with literal and paraphrastic cases of plagiarism.