Description:The present invention pertains to the interdisciplinary domain of data science, specifically focusing on the intersection of machine learning and graph theory. More specifically, it relates to the utilization of graph-based representations and techniques for classification tasks, with a particular emphasis on incorporating social network analysis principles to enhance classification accuracy and interpretability. The invention finds application in various fields where relational data analysis is crucial, including but not limited to social media analysis, fraud detection, recommendation systems, and bioinformatics.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

In recent years, the proliferation of interconnected data has presented challenges and opportunities in the field of data analysis and machine learning. Traditional methods often struggle to effectively capture the complex relationships and structures inherent in such data. Graph theory, a branch of mathematics concerned with the study of graphs representing networks of interconnected nodes, offers a powerful framework for modeling and analyzing relational data.

Simultaneously, machine learning techniques have seen significant advancements, allowing for the development of sophisticated models capable of handling large volumes of data. However, the integration of machine learning with graph theory remains an area of active research with immense potential.

In various domains such as social media, finance, and biology, data can be naturally represented as graphs, where entities are represented as nodes and relationships between them as edges. Social network analysis, a subfield of network science, provides valuable tools and metrics for analyzing the structure and dynamics of such graphs.

The background of the invention lies in the recognition of the need for more effective methods to leverage graph-based representations and social network analysis techniques in conjunction with machine learning for classification tasks. By combining these approaches, it becomes possible to exploit the rich relational information present in the data, leading to more accurate and interpretable classification models.

Existing approaches often focus solely on either graph-based methods or traditional machine learning techniques, overlooking the potential synergies between the two. The present invention addresses this gap by proposing a unified framework that seamlessly integrates graph-based features and social network metrics with machine learning algorithms.

OBJECTIVE OF THE INVENTION

Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.

The primary objective of the invention is to bridge the gap between machine learning methodologies and graph theory, particularly focusing on social network analysis-based classification tasks. By integrating these disciplines, the invention aims to enhance the accuracy, interpretability, and scalability of classification models. The overarching goal is to develop a unified framework that harnesses the relational information encoded in graph-based representations to provide more effective and insightful solutions for analyzing complex datasets.
The invention seeks to advance the state-of-the-art in data analysis by offering a flexible and generalizable methodology applicable across diverse domains. Through innovative algorithms and techniques, the objective is to empower users to tackle real-world classification challenges more efficiently and accurately, thereby unlocking new opportunities for knowledge discovery and decision-making. Ultimately, the invention strives to facilitate the extraction of actionable insights from interconnected data, enabling informed decision-making and driving progress in fields ranging from social media analytics to fraud detection and beyond.
SUMMARY OF THE INVENTION
This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

In an aspect, the invention presents a methodology that integrates machine learning with graph theory, specifically targeting classification tasks enriched by social network analysis. By treating data as graphs and leveraging graph-based features alongside social network metrics, the invention offers a comprehensive framework for more accurate and interpretable classification. This approach enhances understanding of relational data structures and enables the development of models capable of capturing complex relationships, thus yielding superior classification performance.
Furthermore, the invention provides a scalable and adaptable solution applicable across diverse domains, from social media to finance and bioinformatics. By seamlessly blending machine learning algorithms with graph-based representations, it empowers users to extract actionable insights from interconnected datasets, fostering informed decision-making and driving innovation in data analysis. Overall, the invention represents a significant advancement in the integration of machine learning and graph theory, promising to revolutionize classification methodologies and unlock new opportunities for knowledge discovery in relational data.
BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary method for classification using machine learning and graph theory, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

The invention proposes a comprehensive methodology that combines machine learning techniques with graph theory, particularly focusing on social network analysis-based classification tasks. The detailed description encompasses several key components:

Graph-Based Representation: The invention begins by representing data as graphs, where entities are nodes and relationships between them are edges. Various types of graphs, such as directed, undirected, weighted, and bipartite graphs, can be utilized depending on the nature of the data. The invention provides methods for converting raw data into graph structures, ensuring compatibility with subsequent analysis.

Feature Extraction: Once data is represented as graphs, the invention employs feature extraction techniques to capture relevant information. Graph-based features are extracted, including node centrality measures (e.g., degree centrality, betweenness centrality), graph motifs, and community structures. Additionally, social network metrics derived from social network analysis principles are computed, such as clustering coefficients, modularity, and PageRank scores.

Machine Learning Integration: The extracted features are then integrated with traditional machine learning algorithms to build classification models. Various machine learning techniques can be employed, including but not limited to decision trees, support vector machines, neural networks, and ensemble methods. The invention provides methods for feature engineering, model selection, and hyperparameter tuning to optimize classification performance.

Interpretability: The invention prioritizes interpretability by enabling users to understand the factors contributing to classification decisions. Graph-based features and social network metrics offer intuitive insights into the underlying structure of the data, facilitating the interpretation of model predictions. Visualization techniques are employed to visualize graph representations, feature importance, and classification results, enhancing the user's understanding.

Scalability and Generalizability: The methodology is designed to be scalable, capable of handling large-scale relational datasets efficiently. By leveraging efficient algorithms and scalable machine learning techniques, the invention ensures computational feasibility for real-world applications. Furthermore, the framework is generalizable across diverse domains, allowing for its application to various classification tasks across different industries.

Applications: The invention finds applications in numerous fields, including social media analysis, fraud detection, recommendation systems, bioinformatics, and more. It can be utilized for tasks such as identifying influential users in social networks, detecting fraudulent activities in financial transactions, recommending personalized content to users, and classifying biomolecular interactions.

In an aspect, the invention offers a detailed methodology for integrating machine learning with graph theory, with a focus on social network analysis-based classification. By providing a structured approach encompassing data representation, feature extraction, machine learning integration, interpretability, scalability, and generalizability, the invention empowers users to tackle complex classification tasks effectively across diverse domains.

In one embodiment, the invention incorporates Graph Neural Networks (GNNs) into the methodology. GNNs are specialized neural network architectures designed to operate directly on graph-structured data, allowing for the propagation of information across nodes in the graph. The invention utilizes GNNs to learn node embeddings that capture the relational information encoded in the graph. These embeddings are then combined with traditional machine learning features and fed into classification models. By leveraging GNNs, the invention can effectively capture complex graph structures and achieve superior classification performance, particularly in scenarios where the data exhibits intricate relational dependencies.

In yet another embodiment, the invention extends the methodology to handle dynamic graphs, where the structure of the graph evolves over time. Traditional machine learning techniques struggle to adapt to dynamic environments where nodes and edges may be added, removed, or modified over time. The invention addresses this challenge by incorporating techniques from temporal graph analysis and dynamic network modeling. By capturing temporal dependencies and evolving graph structures, the invention enables the development of classification models that are robust to changes over time. This embodiment finds applications in domains such as social media dynamics, where relationships between users evolve continuously, and timely classification is essential for decision-making.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.
, Claims:1. A method for classification using machine learning and graph theory, comprising:
Representing data as a graph, wherein nodes represent entities and edges represent relationships between entities;
Extracting graph-based features from the data, including but not limited to node centrality measures, graph motifs, and community structures;
Integrating graph-based features with traditional machine learning algorithms to build classification models;
Applying the classification models to predict labels or categories for new instances.

2. The method of claim 1, wherein the graph-based features are utilized in conjunction with social network analysis metrics to enhance classification accuracy and interpretability.

3. A computer-implemented system for classification using machine learning and graph theory, comprising: a. A data preprocessing module configured to convert raw data into graph representations. b. A feature extraction module configured to extract graph-based features from the graph representations. c. A machine learning module configured to integrate the graph-based features with traditional machine learning algorithms for classification tasks. d. A prediction module configured to apply the trained classification models to predict labels or categories for new data instances.

4. The system of claim 3, further comprising a visualization module configured to display the graph representations and classification results in a user-friendly interface.

5. A computer-readable storage medium containing instructions that, when executed by a processor, cause the processor to perform the method of classification using machine learning and graph theory as described in claim 1.

6. A method for analyzing social networks, comprising: a. Representing a social network as a graph, wherein nodes represent individuals and edges represent interactions between individuals. b. Extracting social network metrics from the graph, including but not limited to centrality measures, community structures, and clustering coefficients. c. Utilizing the social network metrics as features in machine learning algorithms for tasks such as community detection, influence identification, and anomaly detection.

7. The method of claim 6, wherein the social network metrics are integrated with graph-based features derived from other sources to enhance the analysis of social networks.

8. A computer program product comprising a non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of analyzing social networks as described in claim 6.