We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks.Different automatic learning algorithms for text categori-zation have different classification accuracy.Very accurate text classifiers can be learned automatically from training examples.

Inductive learning algorithms and representations for text categorization

In this paper, we introduce a method to detect co-saliency from an image pair that may have some objects in common. The co-saliency is modeled as a linear combination of the single-image saliency map (SISM) and the multi-image saliency map (MISM). The first term is designed to describe the local attention, which is computed by using three saliency detection techniques available in literature. To compute the MISM, a co-multilayer graph is constructed by dividing the image pair into a spatial pyramid representation. Each node in the graph is described by two types of visual descriptors, which are extracted from a representation of some aspects of local appearance, e.g., color and texture properties. In order to evaluate the similarity between two nodes, we employ a normalized single-pair SimRank algorithm to compute the similarity score. Experimental evaluation on a number of image pairs demonstrates the good performance of the proposed method on the co-saliency detection task.

A Co-Saliency Model of Image Pairs

In recent years, a web phenomenon known as Volunteered Geographic Information (VGI) has produced large crowdsourced geographic data sets OpenStreetMap (OSM), the leading VGI project, aims at building an open-content world map through user contributions OSM semantics consists of a set of properties (called ‘tags’) describing geographic classes, whose usage is defined by project contributors on a dedicated Wiki website Because of its simple and open semantic structure, the OSM approach often results in noisy and ambiguous data, limiting its usability for analysis in information retrieval, recommender systems and data mining Devising a mechanism for computing the semantic similarity of the OSM geographic classes can help alleviate this semantic gap The contribution of this paper is twofold It consists of (1) the development of the OSM Semantic Network by means of a web crawler tailored to the OSM Wiki website; this semantic network can be used to compute semantic similarity through co-citation measures, providing a novel semantic tool for OSM and GIS communities; (2) a study of the cognitive plausibility (ie the ability to replicate human judgement) of co-citation algorithms when applied to the computation of semantic similarity of geographic concepts Empirical evidence supports the usage of co-citation algorithms—SimRank showing the highest plausibility—to compute concept similarity in a crowdsourced semantic network

/pdf/geographic-knowledge-extraction-and-semantic-similarity-in-263ogvb4mk.pdf

Geographic knowledge extraction and semantic similarity in OpenStreetMap

The growing ubiquity of social networks has spurred research in link prediction, which aims to predict new connections based on existing ones in the network. The 2011 IJCNN Social Network challenge asked participants to separate real edges from fake in a set of 8960 edges sampled from an anonymized, directed graph depicting a subset of relationships on Flickr. Our method incorporates 94 distinct graph features, used as input for classification with Random Forests. We present a three-pronged approach to the link prediction task, along with several novel variations on established similarity metrics. We discuss the challenges of processing a graph with more than a million nodes. We found that the best classification results were achieved through the combination of a large number of features that model different aspects of the graph structure. Our method achieved an area under the receiver-operator characteristic (ROC) curve of 0.9695, the 2nd best overall score in the competition and the best score which did not de-anonymize the dataset.

Graph-based features for supervised link prediction

SimRank is an intuitive and effective measure for link-based similarity that scores similarity between two nodes as the first-meeting probability of two random surfers, based on the random surfer model. However, when a user queries the similarity of a given node-pair based on SimRank, the existing approaches need to compute the similarities of other node-pairs beforehand, which we call an all-pair style. In this paper, we propose a Single-Pair SimRank approach. Without accuracy loss, this approach performs an iterative computation to obtain the similarity of a single node-pair. The time cost of our Single-Pair SimRank is always less than All-Pair SimRank and obviously efficient when we only need to assess similarity of one or a few node-pairs. We confirm the accuracy and efficiency of our approach in extensive experimental studies over synthetic and real datasets.

https://epubs.siam.org/doi/pdf/10.1137/1.9781611972801.50

Fast Single-Pair SimRank Computation.

In many real-world domains, link graph is one of the most effective ways to model the relationships between objects. Measuring the similarity of objects in a link graph is studied by many researchers, but an effective and efficient method is still expected. Based on our observation of link graphs from real domains, we find the block structure naturally exists. We propose an algorithm called BlockSimRank , which partitions the link graph into blocks, and obtains similarity of each node-pair in the graph efficiently. Our method is based on random walk on two-layer model, with time complexity as low as O (n 4/3) and less memory need. Experiments show that the accuracy of BlockSimRank is acceptable when the time cost is the lowest.

Exploiting the Block Structure of Link Graph for Efficient Similarity Computation

Assessing single-pair similarity over graphs by aggregating first-meeting probabilities

Tagging behavior on the Internet has seen dramatic increase in recent years, and social tagging has become a popular way to organize and share resources. However, ambiguity and large quantities of tags restrict its effective use for resource searching and classifying. Tag clustering can group tags with similar semantics together, thus helping alleviate these problems. In this paper, we introduce a random walk-based method to measure relevance between tags by exploiting the relationship between tags and resources. Based on this, we also develop a novel clustering method, TagClus, which can address several challenges in tag clustering. Experimental results on a real dataset show that our methods achieve good accuracy and acceptable performance for tag clustering.

TagClus : a random walk-based method for tag clustering

Both Content analysis and link analysis have its advantages in measuring relationships among documents. In this paper, we propose a new method to combine these two methods to compute the similarity of research papers so that we can do clustering of these papers more accurately. In order to improve the efficiency of similarity calculation, we develop a strategy to deal with the relationship graph separately without affecting the accuracy. We also design an approach to assign different weights to different links to the papers, which can enhance the accuracy of similarity calculation. The experimental results conducted on ACM Data Set show that our new algorithm, S-SimRank,outperforms other algorithms.

Pei Li

Papers

Fast Single-Pair SimRank Computation.

Exploiting the Block Structure of Link Graph for Efficient Similarity Computation

Assessing single-pair similarity over graphs by aggregating first-meeting probabilities

TagClus : a random walk-based method for tag clustering

S-SimRank: Combining Content and Link Information to Cluster Papers Effectively and Efficiently