Algorithms and applications of Link Mining: Making Sense of Network Data

链接挖掘是指一组数据挖掘算法和技术，通过这些算法和技术，我们可以从网络或链接数据中学习重要的模式。链接可以用不同的维度表示。链接可以是定向的或双向的，有签名的或无签名的，有权重的或无权重的。链接挖掘任务是使用一个或多个链接维度（链接存在、方向、符号、权重、类和内容）来预测另一个缺失的维度。另一个研究得很好的链接挖掘任务与网络的超结构分析有关，如社区检测和聚类。链路挖掘包括几个具有挑战性的任务，如链路预测、符号和方向预测、节点排序和社区检测。其他任务包括弱联系分析、检测信息代理和节点排序。上述所有任务都是通过链路维度、节点特征和交换内容来完成的。在链路挖掘任务中，链路预测备受关注。链路预测是一种预测现有节点或新节点之间新连接可能性的算法。链接预测有许多应用，例如：分析网络增长，专家识别协作网络（如引用和合著网络），在在线约会中寻找完美匹配，以及在社交网络中揭示某些链接被故意删除的缺失链接。链路预测可以在冷启动或热启动场景下实现。冷启动场景是指我们希望在一组未连接的节点中找到底层网络或社会结构（在现在或未来）的问题。另一方面，热启动是一个基于其他可用链接（或网络结构）向新用户或现有用户推荐链接的问题。链接挖掘在社会网络分析、流行病分析、推荐系统、体育分析和自然语言处理中的实体链接提取等方面都有应用。大多数自然和人工网络都有稀疏的架构。网络的稀疏性增加了链路挖掘算法的复杂性，特别是当使用监督学习技术时。链路挖掘任务的另一个挑战是缺乏训练数据。另一种方法是推进半监督学习和远程监督。提出的研究目标有四个方面：1)将链接挖掘和预测任务引入新的应用，如法律研究、流行病学和体育分析。2)采用语言建模技术和强大的NLP和深度学习工具来学习复杂网络中的潜在模式。3)开发一种现代元学习算法来处理链路预测任务中训练数据的复杂性。4)利用线形图变换复杂网络，直接进行链接嵌入。

Link mining refers to a set of data mining algorithms and techniques by which we can learn non-trivial patterns from a network or linked data. Links can be represented by various dimensions. Links can be directional or bi-directional, signed or unsigned, weighted or unweighted. The link mining task is to use one or more link dimensions (link existence, direction, sign, weight, class and content) to predict another missing dimension. Another well-studied link mining task is related to the hyper-structure analysis of the networks such as community detection and clustering. Link mining includes several challenging tasks such as link prediction, sign and direction prediction, node ranking, and community detection. Other tasks include weak-tie analysis, detecting information brokers and node ranking. All the above-mentioned tasks are conducted using link dimensions using node characteristics and exchanging content. Among the link mining task, link prediction has drawn much attention. Link prediction is an algorithm that predicts the likelihood of new ties among existing or new nodes. Link prediction has many applications such as: analyzing network growth, expert identification in collaboration networks such as citation and co-authorship networks, finding perfect matches in online dating, and revealing missing links in social networks in which some of the links intentionally removed. Link prediction is implemented either in a cold start or warm start scenario. Cold start scenario refers to a problem in which we want to find the underlying network or social fabric (in the present time or future) among a set of non-connected nodes. On the other hand, a warm start is a problem of recommending links to new or existing users based on other available links (or network structure). Link mining has applications in social network analysis, epidemic analysis, recommender systems, sports analytics, and entity link extraction in natural language processing. The majority of natural and man-made networks have sparse architectures. The network sparsity increases the complexity of link mining algorithms especially when supervised learning techniques are employed. The other challenge in link mining tasks is the lack of training data. One alternative approach is to advance semi-supervised learning and distant supervision. The objectives of the proposed research are four-fold: 1) Introducing link mining and prediction tasks to new applications such as legal research, epidemiology and sports analytics. 2) Adopting language modelling techniques and powerful NLP and deep learning tools for learning about latent patterns in complex networks. 3) Developing a modern meta-learning algorithm to deal with training data complexities in link prediction tasks. 4) Leveraging line graphs to transform complex networks and to directly perform link embedding.