III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation

网络数据在现实世界中无处不在，许多提供各种服务的在线网站都可以表示为网络，例如在线社交网络、电子商务网络和学术网络。网络结构数据的学习和挖掘是近年来研究的热点问题之一，也是具有挑战性的研究课题之一。该项目将研究如何为每个网络节点找到一个简单而有效的表示，该表示能够基于其连接来捕捉其在网络中的特征或角色。这被称为网络嵌入问题。网络嵌入是将网络数据转换为经典特征向量表示的有效工具，其目的是将网络数据映射到低维特征空间，即每个网络节点具有较少的特征。有了嵌入的结果，所有上述网络都将受益于改善为公众提供的服务。本项目致力于开发一个通用的网络嵌入框架，并研究其对面向应用、多网络和动态网络场景的扩展。该项目将帮助支持女性和少数民族学生参与关于网络嵌入的学术研究。由于多种原因，本课题研究的网络嵌入是一项具有挑战性的学习任务。(1)数据视角，现实社会网络数据的异构性使得现有的面向同质网络的嵌入模型无法工作；(2)结构保持视角，许多基于一阶邻近度的嵌入方法很难保持复杂的社会网络结构和异质节点类型；(3)任务视角，嵌入过程与外部任务的分离使得学习结果对于具有特定目标的应用任务无效。本项目旨在通过提出一种新颖的可扩展的异质社会网络嵌入模型来应对这些挑战，该模型能够有效地将外部任务的目标纳入学习过程。该项目包括五个主要主题：(1)可扩展的异构网嵌入基础；(2)面向应用的单一异构网嵌入；(3)多个异构网间的网络对齐嵌入；(4)面向朋友推荐的动态异构网嵌入；(5)高级可扩展的异构网嵌入技术探索。该项目将极大地丰富社交网络挖掘和数据挖掘的基本原理和技术。就更广泛的影响而言，网络嵌入分析方面的进步对理解社交网络的行为和活动具有根本性进步的变革性潜力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Network data is ubiquitous in the real-world, and many online websites providing various kinds services can all be represented as networks, e.g., online social networks, e-commerce networks, and academic networks. Learning and mining of network structured data have been one of the most popular yet challenging research problems studied in recent years. This project will study the problem of how to find a simple, yet effective representation for each network node, which can capture its characteristics or role in the network based on its connections. This is referred to as the network embedding problem. As an effective tool to transform network data into classic feature-vector representations, network embedding aims at mapping the network data into a low-dimensional feature space, i.e., with a small number of features for each network node. With the embedding results, all these aforementioned networks will be benefited to improve their services provided for the public. This project focuses on developing a general network embedding framework, and investigating its extension to application-oriented, multi-network and dynamic-network scenarios. This project will help support female and minority students to participate in academic research about network embedding. Network embedding studied in this project is a challenging learning task due to many reasons. (1) Data perspective, the heterogeneity of real-world social network data renders existing homogeneous-network oriented embedding models failing to work; (2) Structure preserving perspective, many first-order proximity based embedding methods can hardly preserve the complex social network structure with heterogeneous node types; and (3) Task perspective, the detachment of embedding process with external tasks makes the learnt results ineffective for application tasks with specific objectives. This project aims at tackling these challenges by proposing a novel extensible heterogeneous social network embedding model, which can effectively incorporate the objectives of external tasks in the learning process. This project covers five main themes: (1) extensible heterogeneous network embedding foundation; (2) application oriented embedding of single heterogeneous network; (3) embedding over multiple heterogeneous network for network alignment; (4) dynamic heterogeneous network embedding for friend recommendation; and (5) advanced scalable heterogeneous network embedding technique exploration. This project will greatly enrich the fundamental principles and technologies of social network mining and data mining. In terms of the broader impact, advances in network embedding analysis have transformative potential for fundamental advances in understanding the behavior and activities of the social networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.