Graph diffusion distance: Properties and efficient computation
نویسندگان
چکیده
منابع مشابه
Fast Computation of Graph Edit Distance
The graph edit distance (GED) is a well-established distance measure widely used in many applications. However, existing methods for the GED computation suffer from several drawbacks including oversized search space, huge memory consumption, and lots of expensive backtracking. In this paper, we present BSS GED, a novel vertex-based mapping method for the GED computation. First, we create a smal...
متن کاملDistance-Based Topological Indices and Double graph
Let $G$ be a connected graph, and let $D[G]$ denote the double graph of $G$. In this paper, we first derive closed-form formulas for different distance based topological indices for $D[G]$ in terms of that of $G$. Finally, as illustration examples, for several special kind of graphs, such as, the complete graph, the path, the cycle, etc., the explicit formulas for some distance based topologica...
متن کاملEfficient Graph Edit Distance Computation and Verification via Anchor-aware Lower Bound Estimation
Graph edit distance (GED) is an important similarity measure adopted in a similarity-based analysis between two graphs, and computing GED is a primitive operator in graph database analysis. Partially due to the NP-hardness, the existing techniques for computing GED are only able to process very small graphs with less than 30 vertices. Motivated by this, in this paper we systematically study the...
متن کاملEfficient Distance Computation Between Non-Convex Objects
This paper describes an efficient algorithm for computing the distance between non-convex objects. Objects are modeled as the union of a set of convex components. From this model we construct a hierarchical bounding representation based on spheres. The distance between objects is determined by computing the distance between pairs of convex components using preexisting techniques. The key to eff...
متن کاملEntropic Affinities: Properties and Efficient Numerical Computation
Gaussian affinities are commonly used in graph-based methods such as spectral clustering or nonlinear embedding. Hinton and Roweis (2003) introduced a way to set the scale individually for each point so that it has a distribution over neighbors with a desired perplexity, or effective number of neighbors. This gives very good affinities that adapt locally to the data but are harder to compute. W...
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: PLOS ONE
سال: 2021
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0249624