‎let $g$ be a graph with vertex set $v(g)$ and edge set $x(g)$ and consider the set $a={0,1}$‎. ‎a mapping $l:v(g)longrightarrow a$ is called binary vertex labeling of $g$ and $l(v)$ is called the label of the vertex $v$ under $l$‎. ‎in this paper we introduce a new kind of graph energy for the binary labeled graph‎, ‎the labeled graph energy $e_{l}(g)$‎. ‎it depends on the underlying graph $g$...

In this paper, we present a label propagation algorithm named ACD for anti-community detection. Experimental results on some real world networks show that our algorithm can obtain higher quality results than other methods.

Learning, re-starting and other techniques of modern SAT solvers have been shown efficient when solving SAT instances from industrial application. The ability to exploit the structure of these instances has been proposed as the responsible of such success. Here we study the modularity of some of these instances, used in the latest SAT competitions. Using a simple label propagation algorithm we ...

We present a graph-based semi-supervised label propagation algorithm for acquiring opendomain labeled classes and their instances from a combination of unstructured and structured text sources. This acquisition method significantly improves coverage compared to a previous set of labeled classes and instances derived from free text, while achieving comparable precision.

Identifying communities has always been a fundamental task in analysis of complex networks. Many methods have been devised over the last decade for detection of communities. Amongst them, the label propagation algorithm brings great scalability together with high accuracy. However, it has one major flaw; when the community structure in the network is not clear enough, it will assign every node ...

Label Propagation, a standard algorithm for semi-supervised classification, suffers from scalability issues involving memory and computation when used with largescale graphs from real-world datasets. In this paper we approach Label Propagation as solution to a system of linear equations which can be implemented as a scalable parallel algorithm using the map-reduce framework. In addition to semi...

Community structure is vital to discover the important structures and potential property of complex networks. In recent years, the increasing quality of local community detection approaches has become a hot spot in the study of complex network due to the advantages of linear time complexity and applicable for large-scale networks. However, there are many shortcomings in these methods such as in...

Learning from complex data is becoming increasingly important, and graph kernels have recently evolved into a rapidly developing branch of learning on structured data. However, previously proposed kernels rely on having discrete node label information. In this paper, we explore the power of continuous node-level features for propagation-based graph kernels. Speci cally, propagation kernels expl...

Multi-label propagation aims to transmit the multi-label information from labeled examples to unlabeled examples based on a weighted graph. Existing methods ignore the specific propagation difficulty of different unlabeled examples and conduct the propagation in an imperfect sequence, leading to the error-prone classification of some difficult examples with uncertain labels. To address this pro...

Label propagation (LP) is a popular graph-based semi-supervised learning framework. Its effectiveness limited by the distribution of prior labels. If there are no objects with labels in parts classes, label has very poor performance. To address this issue, we propose based on bipartite graph (LPBBG) algorithm. This approach try to learn as exemplar constraints that reflect relations between and...