11 research outputs found
NetLSD: Hearing the Shape of a Graph
Comparison among graphs is ubiquitous in graph analytics. However, it is a
hard task in terms of the expressiveness of the employed similarity measure and
the efficiency of its computation. Ideally, graph comparison should be
invariant to the order of nodes and the sizes of compared graphs, adaptive to
the scale of graph patterns, and scalable. Unfortunately, these properties have
not been addressed together. Graph comparisons still rely on direct approaches,
graph kernels, or representation-based methods, which are all inefficient and
impractical for large graph collections.
In this paper, we propose the Network Laplacian Spectral Descriptor (NetLSD):
the first, to our knowledge, permutation- and size-invariant, scale-adaptive,
and efficiently computable graph representation method that allows for
straightforward comparisons of large graphs. NetLSD extracts a compact
signature that inherits the formal properties of the Laplacian spectrum,
specifically its heat or wave kernel; thus, it hears the shape of a graph. Our
evaluation on a variety of real-world graphs demonstrates that it outperforms
previous works in both expressiveness and efficiency.Comment: KDD '18: The 24th ACM SIGKDD International Conference on Knowledge
Discovery & Data Mining, August 19--23, 2018, London, United Kingdo
GEDLIB: Une bibliothèque C++ pour le calcul de la distance d'édition sur graphes
International audienceThe graph edit distance (GED) is a flexible graph dissimilarity measure widely used within the structural pattern recognition field. In this paper, we present GEDLIB, a C++ library for exactly or approximately computing GED. Many existing algorithms for GED are already implemented in GEDLIB. Moreover, GEDLIB is designed to be easily extensible: for implementing new edit cost functions and GED algorithms, it suffices to implement abstract classes contained in the library. For implementing these extensions, the user has access to a wide range of utilities, such as deep neural networks, support vector machines, mixed integer linear programming solvers, a blackbox optimizer, and solvers for the linear sum assignment problem with and without error-correction
Upper Bounding the Graph Edit Distance Based on Rings and Machine Learning
The graph edit distance (GED) is a flexible distance measure which is widely
used for inexact graph matching. Since its exact computation is NP-hard,
heuristics are used in practice. A popular approach is to obtain upper bounds
for GED via transformations to the linear sum assignment problem with
error-correction (LSAPE). Typically, local structures and distances between
them are employed for carrying out this transformation, but recently also
machine learning techniques have been used. In this paper, we formally define a
unifying framework LSAPE-GED for transformations from GED to LSAPE. We also
introduce rings, a new kind of local structures designed for graphs where most
information resides in the topology rather than in the node labels.
Furthermore, we propose two new ring based heuristics RING and RING-ML, which
instantiate LSAPE-GED using the traditional and the machine learning based
approach for transforming GED to LSAPE, respectively. Extensive experiments
show that using rings for upper bounding GED significantly improves the state
of the art on datasets where most information resides in the graphs'
topologies. This closes the gap between fast but rather inaccurate LSAPE based
heuristics and more accurate but significantly slower GED algorithms based on
local search
A survey on the development status and application prospects of knowledge graph in smart grids
With the advent of the electric power big data era, semantic interoperability
and interconnection of power data have received extensive attention. Knowledge
graph technology is a new method describing the complex relationships between
concepts and entities in the objective world, which is widely concerned because
of its robust knowledge inference ability. Especially with the proliferation of
measurement devices and exponential growth of electric power data empowers,
electric power knowledge graph provides new opportunities to solve the
contradictions between the massive power resources and the continuously
increasing demands for intelligent applications. In an attempt to fulfil the
potential of knowledge graph and deal with the various challenges faced, as
well as to obtain insights to achieve business applications of smart grids,
this work first presents a holistic study of knowledge-driven intelligent
application integration. Specifically, a detailed overview of electric power
knowledge mining is provided. Then, the overview of the knowledge graph in
smart grids is introduced. Moreover, the architecture of the big knowledge
graph platform for smart grids and critical technologies are described.
Furthermore, this paper comprehensively elaborates on the application prospects
leveraged by knowledge graph oriented to smart grids, power consumer service,
decision-making in dispatching, and operation and maintenance of power
equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio