Data Mining_ Concepts and Techniques - Jiawei Han [357]
Similarly, link prediction can be performed based on the assessment of the ranking of the expected relationships among the candidate nodes/links. For example, we may predict which papers an author may write, read, or cite, based on the author's recent publication history and the trend of research on similar topics. Such studies often require analyzing the proximity of network nodes/links and the trends and connections of their similar neighbors. Roughly speaking, people refer to link prediction as link mining; however, link mining covers additional tasks includinglink-based object classification, object type prediction, link type prediction, link existence prediction, link cardinality estimation, and object reconciliation (which predicts whether two objects are, in fact, the same). It also includes group detection (which clusters objects), as well as subgraph identification (which finds characteristic subgraphs within networks) and metadata mining (which uncovers schema-type information regarding unstructured data).
Similarity Search and OLAP in Information Networks
Similarity search is a primitive operation in database and web search engines. A heterogeneous information network consists of multityped, interconnected objects. Examples include bibliographic networks and social media networks, where two objects are considered similar if they are linked in a similar way with multityped objects. In general, object similarity within a network can be determined based on network structures and object properties, and with similarity measures. Moreover, network clusters and hierarchical network structures help organize objects in a network and identify subcommunities, as well as facilitate similarity search. Furthermore, similarity can be defined differently per user. By considering different linkage paths, we can derive various similarity semantics in a network, which is known as path-based similarity.
By organizing networks based on the notion of similarity and clusters, we can generate multiple hierarchies within a network. Online analytical processing (OLAP) can then be performed. For example, we can drill down or dice information networks based on different levels of abstraction and different angles of views. OLAP operations may generate multiple, interrelated networks. The relationships among such networks may disclose interesting hidden semantics.
Evolution of Social and Information Networks
Networks are dynamic and constantly evolving. Detecting evolving communities and evolving regularities or anomalies in homogeneous or heterogeneous networks can help people better understand the structural evolution of networks and predict trends and irregularities in evolving networks. For homogeneous networks, the evolving communities discovered are subnetworks consisting of objects of the same type such as a set of friends or coauthors. However, for heterogeneous networks, the communities discovered are subnetworks consisting of objects of different types, such as a connected set of papers, authors, venues, and terms, from which we can also derive a set of evolving objects for each type, like evolving authors and themes.
13.1.3. Mining Other Kinds of Data
In addition to sequences and graphs, there are many other kinds of semi-structured or unstructured data, such as spatiotemporal, multimedia, and hypertext data, which have interesting applications. Such data carry various kinds of semantics, are either stored in or dynamically streamed through a system, and call for specialized data mining methodologies. Thus, mining multiple kinds of data, including spatial data, spatiotemporal data, cyber-physical system data, multimedia data, text data, web data, and data streams, are increasingly important tasks in data mining. In this subsection, we overview the methodologies for mining