Heuristics for Social Network Analysis

社交网络已被用于模拟各种各样的现实生活现象。社交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来解释它们的出现。复杂性和动态性是这种网络的基本属性。由于这些网络通过新链接和节点的出现或消失而随着时间的推移快速发展，因此连接随着底层网络结构的变化而变得更强或更弱。研究人员开始研究社交网络分析中的各种问题，如节点分类，社区检测，团队组建和节点之间的链接预测。社交网络的普遍存在和日益普及提出了以有效的方式分析大量增加的社交交互数据集的挑战性任务。大量的知识可以被提取出来并呈现给领域专家，以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，包括混合方法以及基于群体的进化算法，用于开拓高效的社区检测和链接预测框架，并在大型，复杂和动态的社交网络中提高准确性。例如，通过研究目标链接的基于公共邻居的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接预测转化为视频分类问题，并支持使用卷积神经网络。另一方面，节点的分类导致集群的形成。链接的预测带来相关性和/或关系的形成。通过研究边缘采样技术，并检查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。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现实价值的决策支持模型，并在各自的领域中对其进行验证。本研究在处理动态、快速增长的网络数据的同时，通过不断创新，追求效率和准确性，开创了社会网络分析中的链接预测和社区检测方法。该计划进一步旨在通过利用基于人口的进化生物学的新创新以及深度学习方法来解决团队组建问题和健康网络。实际成果包括从基准测试中改编新算法，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，可供医疗保健领导者等其他学科的领域专家使用。

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.