Graph Edit Distance Learning via Modeling Optimum Matchings with Constraints

Graph edit distance (GED) is a fundamental measure for graph similarity analysis in many real applications. GED computation has known to be NP-hard and many heuristic methods are proposed. GED has two inherent characteristics: multiple optimum node matchings and one-to-one node matching constraints. However, these two characteristics have not been well considered in the existing learning-based methods, which leads to suboptimal models. In this paper, we propose a novel GED-specific loss function that simultaneously encodes the two characteristics. First, we propose an optimal partial node matching-based regularizer to encode multiple optimum node matchings. Second, we propose a plane intersection-based regularizer to impose the one-to-one constraints for the encoded node matchings. We use the graph neural network on the association graph of the two input graphs to learn the cross-graph representation. Our experiments show that our method is 4.2x-103.8x more accurate than the state-of-the-art methods on real-world benchmark graphs.

An Integrated Geometric and Topological Approach for the Identification and Visual Analysis of Rossby Wave Packets

A new method for identifying Rossby wave packets (RWPs) using 6-hourly data from the ERA-Interim is presented. The method operates entirely in the spatial domain and relies on the geometric and topological properties of the meridional wind field to identify RWPs. The method represents RWPs as nodes and edges of a dual graph instead of the more common envelope representation. This novel representation allows access to both RWP phase and amplitude information. Local maxima and minima of the meridional wind field are collected into groups. Each group, called a υ-max cluster or υ-min cluster of the meridional wind field, represents a potential wave component. Nodes of the dual graph represent a υ-max cluster or υ-min cluster. Alternating υ-max clusters and υ-min clusters are linked by edges of the dual graph, called the RWP association graph. Amplitude and discrete gradient-based filtering applied on the association graph helps identify RWPs of interest. The method is inherently robust against noise and does not require smoothing of the input data. The main parameters that control the performance of the method and their impact on the identified RWPs are discussed. All filtering and RWP identification operations are performed on the association graph as opposed to directly on the wind field, leading to computational efficiency. Advantages and limitations of the method are discussed and are compared against (transform-based) envelope methods in a series of experiments.

Semantic Component Association within Object Classes Based on Convex Polyhedrons

Most objects are composed of semantically distinctive parts that are more or less geometrically distinctive as well. Points on the object relevant for a certain robot operation are usually determined by various physical properties of the object, such as its dimensions or weight distribution, and by the purpose of object parts. A robot operation defined for a particular part of a representative object can be transferred and adapted to other instances of the same object class by detecting the corresponding components. In this paper, a method for semantic association of the object’s components within the object class is proposed. It is suitable for real-time robotic tasks and requires only a few previously annotated representative models. The proposed approach is based on the component association graph and a novel descriptor that describes the geometrical arrangement of the components. The method is experimentally evaluated on a challenging benchmark dataset.

Developing a Customer Model for Targeted Marketing using Association Graph Mining

Data mining is the procedure to find out significant information from large database by applying several mining techniques. Finding out products that are purchased together is a major issue in basket market analysis. So, developing a customer model is important for targeted marketing. The traditional dataset is taken into account because the origin of information which is available from the history of sales repository. While applying the basic techniques on transactional data analysis, it fails when the process has a greater number of transactional information. Also, it is difficult to identify suitable correlation between one product to another. In this paper Market Basket Analysis is extended towards into network level and it recommends a product network consideration it clearly states that the correlation involving products bought together by customer. This research work focuses on product to product network analysis in market basket network. The direct and indirect approach is applied in associated product network from history of retailer data. The major intention of this research work is to find the group of essential products purchase by the customer together. So, it will bring out consumer profile, product blue print, guidance from associated products and provide effective result from large number of customer wholesale outline