Master of Science, The Ohio State University, 2019, Computer Science and Engineering

There is growing interest for learning representations for nodes in a network. Several embedding generation algorithms have been proposed in the last few years that generate high quality representations for downstream tasks like node classification and link prediction. NetMF is one such algorithm that provides the theoretical foundations for proving that several network representation learning techniques implicitly factorize a closed form matrix derived from the graph. However, the NetMF algorithm is slow and does not scale well, owing to the multiple dense matrix multiplication steps and Singular Value Decomposition (SVD). We present Fast-NetMF, a fast, highly scalable version of the NetMF algorithm with reduced running time. In this work, we investigate the acceleration of NetMF under single-GPU and multi-core CPU settings. We also investigate replacing the slow SVD based matrix factorization step for faster and more parallel-friendly factorization techniques like Non-negative Matrix Factorization (NMF).

Committee: Srinivasan Parthasarathy (Advisor); Sadayappan P (Committee Member) Subjects: Computer Engineering; Computer Science

Doctor of Philosophy, Case Western Reserve University, 2024, EECS - Computer and Information Sciences

With the rapid development of biotechnology that generate complex biological data, as well as graph machine learning algorithms to analyze these data, network-based analyses are becoming popular in modern biological research. Many network-based methods have been proposed to process biological data. Network embedding, which learns a representation of nodes into a low-dimensional space, has been a new learning paradigm in the studies of network analysis. Graph Representation learning algorithms are being commonly applied to a broad range of prediction tasks in systems biology. Mapping biological networks into a low-dimensional space enables efective application of machine learning methods in the downstream tasks. In this dissertation we present four new algorithms to compare and integrate biological networks, with a view to improving the reliability of graph machine learning algorithms on biological networks. These algorithms address inter-related problems to provide a comprehensive framework for network integration. Many real-world networks that are used in machine learning have multiple versions that come from diferent sources. For such networks, computation of Consensus Embeddings based on the node embeddings of individual versions can be useful for various reasons. GraphCan is a framework for computing canonical representations for biological networks using a similarity-based Graph Convolutional Network, and it integrates multiple node similarity measures to compute canonical node embeddings for a given network. Consensus embedding is used in our GraphCan model to integrate multiple node similarity measures to compute canonical node embeddings for a given network. GraphCan can be applied to diferent types of downstream tasks. BiGraphCan is an extension of GraphCan, and it aims to make bipartite predictions for understudied proteins using similarity networks and bipartite networks. We also explore network alignment problem by generalizing the Gromov-Wasserstein a (open full item for complete abstract)

Committee: Mehmet Koyuturk (Advisor); Rong Xu (Committee Member); Yinghui Wu (Committee Member); Jing Li (Committee Member) Subjects: Computer Science

Master of Science (MS), Wright State University, 2021, Computer Science

Link prediction in the domain of scientific collaborative networks refers to exploring and determining whether a connection between two entities in an academic network may emerge in the future. This study aims to analyze the relevance of academic collaborations and identify the factors that drive co-author relationships in a heterogeneous bibliographic network. Using topological, semantic, and graph representation learning techniques, we measure the authors' similarities w.r.t their structural and publication data to identify the reasons that promote co-authorships. Experimental results show that the proposed approach successfully infer the co-author links by identifying authors with similar research interests. Such a system can be used to recommend potential collaborations among the authors.

Committee: Tanvi Banerjee Ph.D. (Advisor); Krishnaprasad Thirunarayan Ph.D. (Committee Member); Michael L. Raymer Ph.D. (Committee Member) Subjects: Artificial Intelligence; Computer Science

Doctor of Philosophy, Case Western Reserve University, 2020, EECS - Computer and Information Sciences

Graphs (or networks) naturally represent valuable information for relational data, which are ubiquitous in real-world applications, such as social networks, recommender systems, and biological networks. Statistical learning or machine learning techniques for network analysis, such as random walk with restart, meta-path analysis, network embeddings, and matrix/tensor factorizations, have gained tremendous attentions recently. With rapid growth of data, networks, either homogeneous or heterogeneous, can consist of billions of nodes and edges. How can we find underlying structures within a network? How can we efficiently manage data when multiple sources describing the networks are available? How can we detect the most important relationships among nodes? To gain insights into these problems, this dissertation investigates the principles and methodologies of dimension reduction techniques that explore the useful latent structures of one or more networks. Our dimension reduction techniques mainly leverage recent developments in linear algebra, graph theory, large-scale optimization, and deep learning. In addition, we also translate our ideas and models to several real-world applications, especially in drug repositioning, drug combinations, and drug-target-disease interactions. For each research problem, we discuss their current challenges, related work, and propose corresponding solutions.

Committee: Jing Li Dr. (Committee Chair); Harold Connamacher Dr. (Committee Member); Xusheng Xiao Dr. (Committee Member); Satya Sahoo Dr. (Committee Member) Subjects: Computer Science

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Computer Science and Engineering

Graphs arise naturally in many complex systems where they are used to represent entities and relationships between them. The analysis of graph-based models has wide applications like evaluating the significance of interactions between individual entities, identifying important subcomponents, discovering hidden interactions, and making complex inferences about the functions of the underlying systems. Many of these applications require meaningful representation of nodes, and several graph-embedding algorithms have recently been developed to embed nodes in meaningful vector spaces. However, it is not clear how the performance of these algorithms depends on the structural features of graphs, which can vary a lot across real world domains. It would thus be useful to identify the main features that influence the performance of embedding approaches, and to develop a method that can determine the most suitable method for any given graph. The research described in this dissertation applies a systematic approach to comparing various graph-embedding methods on several types of graphs, relates their performance to the structural features of the graphs, and develops a system to select the best embedding method based on graph features. By evaluating the node embedding algorithms for link prediction on several synthetic graph models and real-world network datasets, this study demonstrates the fact that the structural properties of a graph have a significant effect on how well any given node embedding algorithm performs on it. For a particular graph, the performance of a node embedding algorithm can be predicted based on the structural properties, and this relationship holds across a wide range of network types and real-world networks. The results in this dissertation lead to several insights about which algorithms work for various types of graphs.

Committee: Ali Minai Ph.D. (Committee Chair); Raj Bhatnagar Ph.D. (Committee Member); Yizong Cheng Ph.D. (Committee Member); Jaroslaw Meller Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Science

Master of Mathematical Sciences, The Ohio State University, 2019, Mathematical Sciences

Many problems in science and engineering involve signal analysis. Engineers and scientists came up with many approaches to study signals. Recently, researchers propose a new frame- work, combining the time-delay embedding with the tools from computational topology, for the study of periodic signals. By applying time-delay embedding to the periodic signals, the periodic behaviors express themselves as topological cycles and we can use persistent homol- ogy to detect these topological features. In this thesis, we apply this method to analyze road network data, specifically vehicle flow data recorded by detectors placed on highways. First, we apply time-delay embedding to project the vehicle flow data into point cloud data in a high dimensional space. Then, we use persistent homology tools to detect the topological features and get persistence digram. Next, we can repeat the same experiment to vehicle flow data of different period. Fox example, in our experiment, we use the vehicle flow data of different weeks and months. Therefore, we get persistence diagrams corresponding to the vehicle flow data of different period. Finally, we calculate the bottleneck distance and wasserstein distance between these persistence diagrams and do hierarchical clustering. The dendrograms of the hierarchical clustering show us the patterns behind these vehicle flow data.

Committee: Facundo Mémoli (Advisor); Yusu Wang (Advisor) Subjects: Mathematics

PhD, University of Cincinnati, 2011, Engineering and Applied Science: Computer Science and Engineering

Graphs serve as an important tool for discrete data representation. Recently, graph representations have made possible very powerful machine learning algorithms, such as manifold learning, kernel methods, semi-supervised learning. With the advent of large-scale real world networks, such as biological networks (disease network, drug target network, etc.), social networks (DBLP Co-authorship network, Facebook friendship, etc.), machine learning and data mining algorithms have found new application areas and have contributed to advance our understanding of properties, and phenomena governing real world networks. When dealing with real world data represented as networks, two problems arise quite naturally: I) How to integrate and align the knowledge encoded in multiple and heterogeneous networks? For instance, how to find out the similar genes in co-disease and protein-protein interaction networks? II) How to model and predict the evolution of a dynamic network? A real world example is, given N years snapshots of an evolving social network, how to build a model that can capture the temporal evolution and make reliable prediction? In this dissertation, we present an innovative graph embedding framework, which identifies the key components of modeling the evolution in time of a dynamic graph. Different from the many state-of-the-art graph link prediction and modeling algorithms, it formulates the link prediction problem from a geometric perspective that can capture the dynamics of the intrinsic continuous graph manifold evolution. It is attractive due to its simplicity and the potential to relax the mining problem into a feasible domain which enables standard machine learning and regression models to utilize historical graph time series data. To address the first problem, we first propose a novel probability-based similarity measure which led to promising applications in content based image retrieval and image annotation, followed by a manifold alignment framework t (open full item for complete abstract)

Committee: Anca Ralescu PhD (Committee Chair); Anil Jegga DVMMRes (Committee Member); Fred Annexstein PhD (Committee Member); Kenneth Berman PhD (Committee Member); Yizong Cheng PhD (Committee Member); Dan Ralescu PhD (Committee Member) Subjects: Computer Science