Social Network Analysis using Cultural Algorithms and its Variants

Finding relationships between social entities and discovering the underlying structures of networks are fundamental tasks for analyzing social networks. In recent years, various methods have been suggested to study these networks efficiently, however, due to the dynamic and complex nature that these networks have, a lot of open problems still exist in the field. The aim of this research is to propose an integrated computational model to study the structure and behavior of the complex social network. The focus of this research work is on two major classic problems in the field which are called community detection and link prediction. Moreover, a problem of population adaptation through knowledge migration in real-life social systems has been identified to model and study through the proposed method. To the best of our knowledge, this is the first work in the field which is exploring this concept through this approach. In this research, a new adaptive knowledge-based evolutionary framework is defined to investigate the structure of social networks by adopting a multi-population cultural algorithm. The core of the model is designed based on a unique community-oriented approach to estimate the existence of a relationship between social entities in the network. In each evolutionary cycle, the normative knowledge is shaped through the extraction of the topological knowledge from the structure of the network. This source of knowledge is utilized for the various network analysis tasks such as estimating the quality of relation between social entities, related studies regarding the link prediction, population adaption, and knowledge formation. The main contributions of this work can be summarized in introducing a novel method to define, extract and represent different sources of knowledge from a snapshot of a given network to determine the range of the optimal solution, and building a probability matrix to show the quality of relations between pairs of actors in the system. Introducing a new similarity metric, utilizing the prior knowledge in dynamic social network analysis and study the co-evolution of societies in a case of individual migration are another major contributions of this work. According to the obtained results, utilizing the proposed approach in community detection problem can reduce the search space size by 80%. It also can improve the accuracy of the search process in high dense networks by up to 30% compared with the other well-known methods. Addressing the link prediction problem through the proposed approach also can reach the comparable results with other methods and predict the next state of the system with a notably high accuracy. In addition, the obtained results from the study of population adaption through knowledge migration indicate that population with prior knowledge about an environment can adapt themselves to the new environment faster than the ones who do not have this knowledge if the level of changes between the two environments is less than 25%. Therefore, utilizing this approach in dynamic social network analysis can reduce the search time and space significantly (up to above 90%), if the snapshots of the system are taken when the level of changes in the network structure is within 25%. In summary, the experimental results indicate that this knowledge-based approach is capable of exploring the evolution and structure of the network with the high level of accuracy while it improves the performance by reducing the search space and processing time

Graph Deep Learning: Methods and Applications

The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone\u27s life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning such as knowledge graph completion and recommender systems, to mutli-graph tasks such as graph classification and graph generation etc.An emerging new field, graph deep learning, aims at applying deep learning to graphs. To deal with graph-structured data, graph neural networks (GNNs) are invented in recent years which directly take graphs as input and output graph/node representations. Although GNNs have shown superior performance than traditional methods in tasks such as semi-supervised node classification, there still exist a wide range of other important graph learning problems where either GNNs\u27 applicabilities have not been explored or GNNs only have less satisfying performance.In this dissertation, we dive deeper into the field of graph deep learning. By developing new algorithms, architectures and theories, we push graph neural networks\u27 boundaries to a much wider range of graph learning problems. The problems we have explored include: 1) graph classification; 2) medical ontology embedding; 3) link prediction; 4) recommender systems; 5) graph generation; and 6) graph structure optimization.We first focus on two graph representation learning problems: graph classification and medical ontology embedding.For graph classification, we develop a novel deep GNN architecture which aggregates node features through a novel SortPooling layer that replaces the simple summing used in previous works. We demonstrate its state-of-the-art graph classification performance on benchmark datasets. For medical ontology embedding, we propose a novel hierarchical attention propagation model, which uses attention mechanism to learn embeddings of medical concepts from hierarchically-structured medical ontologies such as ICD-9 and CCS. We validate the learned embeddings on sequential procedure/diagnosis prediction tasks with real patient data.Then we investigate GNNs\u27 potential for predicting relations, specifically link prediction and recommender systems. For link prediction, we first develop a theory unifying various traditional link prediction heuristics, and then design a framework to automatically learn suitable heuristics from a given network based on GNNs. Our model shows unprecedented strong link prediction performance, significantly outperforming all traditional methods. For recommender systems, we propose a novel graph-based matrix completion model, which uses a GNN to learn graph structure features from the bipartite graph formed by user and item interactions. Our model not only outperforms various matrix completion baselines, but also demonstrates excellent transfer learning ability -- a model trained on MovieLens can be directly used to predict Douban movie ratings with high performance.Finally, we explore GNNs\u27 applicability to graph generation and graph structure optimization. We focus on a specific type of graphs which usually carry computations on them, namely directed acyclic graphs (DAGs). We develop a variational autoencoder (VAE) for DAGs and prove that it can injectively map computations into a latent space. This injectivity allows us to perform optimization in the continuous latent space instead of the original discrete structure space. We then apply our VAE to two types of DAGs, neural network architectures and Bayesian networks. Experiments show that our model not only generates novel and valid DAGs, but also finds high-quality neural architectures and Bayesian networks through performing Bayesian optimization in its latent space

Negative Link Prediction in Social Media

Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework