Optimizing regular computations based on neural networks and Graph Traversal
نویسندگان
چکیده
منابع مشابه
Link Prediction Based on Graph Neural Networks
Traditional methods for link prediction can be categorized into three main types: graph structure feature-based, latent feature-based, and explicit feature-based. Graph structure feature methods leverage some handcrafted node proximity scores, e.g., common neighbors, to estimate the likelihood of links. Latent feature methods rely on factorizing networks’ matrix representations to learn an embe...
متن کاملOn Graph-based Cryptography and Symbolic Computations
We have been investigating the cryptographical properties of infinite families of simple graphs of large girth with the special colouring of vertices during the last 10 years. Such families can be used for the development of cryptographical algorithms (on symmetric or public key modes) and turbocodes in error correction theory. Only few families of simple graphs of large unbounded girth and arb...
متن کاملOptimizing Regular Path Expressions Using Graph Schemas
Several languages, such as LOREL and UnQL, support querying of semi-structured data. Others, such as WebSQL and WebLog, query Web sites. All these languages model data as labeled graphs and use regular path expressions to express queries that traverse arbitrary paths in graphs. Naive execution of path expressions is ineecient, however, because it often requires exhaustive graph search. We descr...
متن کاملGraph Representations and Traversal
A graph G = (V,E), where V is the set of vertices, and E is the set of edges. An edge e ∈ E is an unordered pair (u,v) in undirected graphs, where u,v∈V . In directed graphs, an edge e is an ordered pair. A path from a vertex u to a vertex v is a sequence of vertices (w0,w1, ...wk), where u = w0, v = wk and (wi−1,wi) ∈ E for all 1 ≤ i ≤ k. The path is a cycle if u = v. The length of a path in a...
متن کاملSelf-Optimizing Neural Networks
The paper is concentrated on two essential problems: neural networks topology optimization and weights parameters computation that are often solved separately. This paper describes new solution of solving both selected problems together. According to proposed methodology a special kind of multilayer ontogenic neural networks called SelfOptimizing Neural Networks (SONNs) can simultaneously devel...
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: Procedia Computer Science
سال: 2021
ISSN: 1877-0509
DOI: 10.1016/j.procs.2021.04.221