Heuristics for Social Network Analysis

社会网络已经被用来模拟广泛的现实生活现象。社会网络分析是对这种网络的研究，以发现共同的结构模式，并通过网络形成的计算模型解释它们的出现。复杂性和动态性是此类网络的基本特性。由于这些网络随着时间的推移通过新链接和节点的出现或消失而快速发展，因此随着潜在网络结构的变化，这些连接会变得更强或更弱。研究人员开始研究社会网络分析中的各种问题，如节点的分类、社区的检测、团队的形成以及节点之间联系的预测。社交网络的普遍存在和日益普及提出了一项具有挑战性的任务，即以有效的方式分析大量增加的社交互动数据集。大量的知识可以被提取出来，并提交到领域专家手中，帮助他们做出重要的决策。然而，如果没有及时和准确的预测，机会就会错过。本研究探索了深度学习方法，包括混合方法和基于群体的进化算法，用于在大型、复杂和动态的社会网络中开拓高效的社区检测和链接预测框架，提高了准确性。例如，通过研究目标链路的共同近邻子图，并使用特征矩阵来学习给定动态网络的过渡模式，我们有效地将动态链路预测转化为视频分类问题，并使卷积神经网络能够使用。另一方面，节点的分类导致了集群的形成。链接的预测带来了相关性和/或联系的形成。通过研究边缘采样技术，并研究具有不同表示学习层的体系结构，我们利用特征提取和降维来增强从网络结构中自动提取事实的能力。通过与来自其他学科的领域专家合作，我们可以有效地将这些框架开发成具有实际价值的决策支持模型，并在各自的领域中验证它们。本研究在处理动态和快速增长的网络数据的同时，不断创新，追求效率和准确性，开创了社会网络分析的链接预测和社区检测方法。该项目进一步旨在通过利用基于种群的进化启发式和深度学习方法的新创新来解决团队组建问题和健康网络。实际成果包括适应基准测试中的新算法，并将其嵌入到基于代理的模型中，作为决策支持的有用工具，可供来自其他学科（如医疗保健领导者）的领域专家使用。

Social networks have been used to model a comprehensive range of real-life phenomena. Social networks analysis is the study of such networks to discover common structural patterns and explains their emergence through computational models of network formation. The complexity and dynamics are essential properties of such networks. Since these networks evolve quickly over time through the appearance or disappearance of new links and nodes, the connections become stronger and weaker with underlying network structure changing over time. Researchers are embarked on examining various issues in social network analysis such as classification of nodes, detection of communities, formation of teams, and prediction of links between nodes. Ubiquity and growing popularity of social networks present the challenging task to analyze the massively increasing datasets of social interactions in an efficient manner. Vast amounts of knowledge can be extracted and presented into the hands of domain experts to help make important decisions. However, without timely and accurate predictions, opportunities are missed. This research explores Deep Learning methods including hybrid approaches as well as population-based evolution algorithms for pioneering efficient community detection and link prediction frameworks with increased accuracy in large, complex, and dynamic social networks. For instance, by studying common neighbors based subgraphs of a target link and using feature matrices for learning the transitional pattern for a given dynamic network, we effectively transform the dynamic link prediction to a video classification problem and enable the use of Convolutional Neural Networks . On another front, classification of nodes induces the formation of clusters. The prediction of links brings about correlations and/or ties formation. By studying edge sampling techniques, and examining an architecture with distinct representation learning layers, we exploit feature-extraction and dimensionality-reduction to enhance automatic extraction of facts from the network structure. By partnering with domain experts from other disciplines we can effectively develop these frameworks into decision support models of real-world value and validate them in their respective domain. This research pioneers link prediction and community detection methods for social network analysis by stepping up innovation, seeking efficiency and accuracy, while handling dynamic and fast increasing network data. The program further aims to tackle team formation problems and health networks by capitalizing on new innovations in population based evolutionary heuristics, as well as deep learning approaches. Practical outcomes include the adaptation of novel algorithms from benchmark tests and embedding them into agent-based models to serve as useful tools for decision support that can be used by domain experts from other disciplines such as healthcare leaders.