dyngraph2vec: Capturing network dynamics using dynamic graph representation learning
نویسندگان
چکیده
منابع مشابه
Graph Dynamics : Learning and Representation by Andre
Graphs are often used in artificial intelligence as means for symbolic knowledge representation. A graph is nothing more than a collection of symbols connected to each other in some fashion. For example, in computer vision a graph with five nodes and some edges can represent a table – where nodes correspond to particular shape descriptors for legs and a top, and edges to particular spatial rela...
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Existing visual tracking methods usually localize a target object with a bounding box, in which the performance of the foreground object trackers or detectors is often affected by the inclusion of background clutter. To handle this problem, we learn a patch-based graph representation for visual tracking. The tracked object is modeled by with a graph by taking a set of non-overlapping image patc...
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Many real-world problems are represented by using graphs. For example, given a graph of a chemical compound, we want do determine whether it causes a gene mutation or not. As another example, given a graph of a social network, we want to predict a potential friendship that does not exist but it is likely to appear soon. Many of these questions can be answered by using machine learning methods i...
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ژورنال
عنوان ژورنال: Knowledge-Based Systems
سال: 2020
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2019.06.024