Analysis and Actions on Graph Data.

Graphs are commonly used for representing relations between entities and handling data processing in various research fields, especially in social, cyber and physical networks. Many data mining and inference tasks can be interpreted as certain actions on the associated graphs, including graph spectral decompositions, and insertions and removals of nodes or edges. For instance, the task of graph clustering is to group similar nodes on a graph, and it can be solved by graph spectral decompositions. The task of cyber attack is to find effective node or edge removals that lead to maximal disruption in network connectivity. In this dissertation, we focus on the following topics in graph data analytics: (1) Fundamental limits of spectral algorithms for graph clustering in single-layer and multilayer graphs. (2) Efficient algorithms for actions on graphs, including graph spectral decompositions and insertions and removals of nodes or edges. (3) Applications to deep community detection, event propagation in online social networks, and topological network resilience for cyber security. For (1), we established fundamental principles governing the performance of graph clustering for both spectral clustering and spectral modularity methods, which play an important role in unsupervised learning and data science. The framework is then extended to multilayer graphs entailing heterogeneous connectivity information. For (2), we developed efficient algorithms for large-scale graph data analytics with theoretical guarantees, and proposed theory-driven methods for automatic model order selection in graph clustering. For (3), we proposed a disruptive method for discovering deep communities in graphs, developed a novel method for analyzing event propagation on Twitter, and devised effective graph-theoretic approaches against explicit and lateral attacks in cyber systems.PHDElectrical & Computer Eng PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135752/1/pinyu_1.pd

Weighted Graph Clustering for Community Detection of Large Social Networks

AbstractThis study mainly focuses on the methodology of weighted graph clustering with the purpose of community detection for large scale networks such as the users’ relationship on Internet social networks. Most of the networks in the real world are weighted networks, so we proposed a graph clustering algorithm based on the concept of density and attractiveness for weighted networks, including node weight and edge weight. With deep analysis on the Sina micro-blog user network and Renren social network, we defined the user's core degree as node weight and users’ attractiveness as edge weight, experiments of community detection were done with the algorithm, the results verify the effectiveness and reliability of the algorithm. The algorithm is designed to make some breakthrough on the time complexity of Internet community detection algorithm, because the research is for large social networks. And the another advantage is that the method does not require to specify the number of clusters

Towards a Computational Model of Narrative on Social Media

This thesis describes a variety of approaches to developing a computational model of narrative on social media. Our goal is to use such a narrative model to identify efforts to manipulate public opinion on social media platforms like Twitter. We present a model in which narratives in a collection of tweets are represented as a graph. Elements from each tweet that are relevant to potential narratives are made into nodes in the graph; for this thesis, we populate graph nodes with tweets’ authors, hashtags, named entities (people, locations, organizations, etc.,), and moral foundations (central moral values framing the discussion). Two nodes are connected with an edge if the narrative elements they represent appear together in one or more tweets, with the edge weight corresponding to the number of tweets in which these elements coincide. We then explore multiple possible deep learning and graph analysis methods for identifying narratives in a collection of tweets, including clustering of language embeddings, topic modeling, community detection and random walks on our narrative graph, training a graph neural network to identify narratives in the graph, and training a graph embedding model to generate vector embeddings of graph nodes. While much work still remains to be done in this area, several of our techniques, especially the generation and clustering of graph embeddings, were able to identify groups of related and connected nodes that might form the beginnings of narratives. Further study of these or other techniques could allow for the reliable identification of full narratives and information operations on social media

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning