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

链接挖掘的算法和应用：理解网络数据

• 批准号: RGPIN-2021-03380
• 负责人: Makrehchi, Masoud
• 金额: $ 1.75万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741819
• 关键词: Algorithms; applications; Link; Mining; Making

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.

链接挖掘是指我们可以从网络或链接数据中学习非平凡模式的一组数据挖掘算法和技术。链接可以用各种维度表示。链接可以是定向或双向，签名或未签名，加权或未加权。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，重量，阶级和内容）来预测另一个缺失的维度。另一个良好的链接挖掘任务与网络的超结构分析有关，例如社区检测和聚类。链接挖掘包括几个具有挑战性的任务，例如链接预测，符号和方向预测，节点排名和社区检测。其他任务包括弱点分析，检测信息经纪和节点排名。所有上述任务均使用节点特征和交换内容的链接维度进行。在链接挖掘任务中，链接预测引起了很多关注。链接预测是一种预测现有节点或新节点之间新联系的可能性的算法。链接预测有许多应用程序，例如：分析网络增长，在协作网络中的专家识别，例如引文和共同创作网络，在在线约会中找到完美的匹配以及在社交网络中揭示了一些有意删除的社交网络中缺少的链接。链接预测是在冷启动或温暖的开始场景中实现的。冷启动方案是指我们希望在一组非连接节点中找到基础网络或社交结构（目前或将来）的问题。另一方面，一个温暖的开始是根据其他可用链接（或网络结构）推荐向新用户或现有用户的链接的问题。 Link Mining在社交网络分析，流行病分析，推荐系统，体育分析和实体链接链接提取中的应用中有应用。大多数自然和人造网络都具有稀疏的建筑。网络稀疏性增加了链接挖掘算法的复杂性，尤其是在采用监督学习技术时。链接挖掘任务的另一个挑战是缺乏培训数据。另一种方法是进步半监督学习和遥远的监督。拟议的研究的目标是四倍：1）将链接挖掘和预测任务引入新应用，例如法律研究，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，以了解复杂网络中的潜在模式。 3）开发一种现代的元学习算法来处理链接预测任务中的培训数据复杂性。 4）利用线图转换复杂的网络并直接执行链接嵌入。