Multi-graph-view subgraph mining for graph classification
نویسندگان
چکیده
منابع مشابه
Cohesive Subgraph Mining on Attributed Graph
Finding cohesive subgraphs is a fundamental graph problem with a wide spectrum of applications. In this paper, we investigate this problem in the context of attributed graph, where each vertex is associated with content (e.g., geo-locations, tags and keywords). To properly capture the cohesiveness of the vertices in a subgraph from both graph structure and vertices attribute perspectives, we ad...
متن کاملMulti-Graph-View Learning for Complicated Object Classification
In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a multi-graph-view model which uses graphs constructed from multiple graph-views to represent an object. In addition, a bag based multi-graph model is further used to relax labeling by only requiring one label for a bag of graphs, which represent one object. In order to learn c...
متن کاملFast multi-view segment graph kernel for object classification
Object classification is an important issue in multimedia information retrieval. Usually, we can use images from multiple views (or multi-view images) to describe an object for classification. However, two issues remain unsolved. First, exploiting the spatial relations of local features from different view images for object classification. Second, accelerating the multi-view object classificati...
متن کاملMultiple Structure-View Learning for Graph Classification.
Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification has been limited to the single-graph setting with each object being represented as one graph from a single structure-view. This inherently...
متن کاملFrequent Subgraph Mining from Streams of Linked Graph Structured Data
Nowadays, high volumes of high-value data (e.g., semantic web data) can be generated and published at a high velocity. A collection of these data can be viewed as a big, interlinked, dynamic graph structure of linked resources. Embedded in them are implicit, previously unknown, and potentially useful knowledge. Hence, efficient knowledge discovery algorithms for mining frequent subgraphs from t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Knowledge and Information Systems
سال: 2015
ISSN: 0219-1377,0219-3116
DOI: 10.1007/s10115-015-0872-1