A novel fuzzy community detection method based on improved community adjacency list

With the development of economy and society, network analysis is widely used in more and more fields. Signed network has a good effect in the process of representation and display. As an important part of network analysis, fuzzy community detection plays an increasingly important role in analyzing and visualizing the real world. Fuzzy community detection helps to detect nodes that belong to some communities but are still closely related to other communities. These nodes are helpful for mining information from the network more realistically. However, there is little research in this field. This paper proposes a fuzzy community detection algorithm based on pointer and adjacency list. The model adopts a new ICALF network data structure, which can achieve the effect of storing community partition structure and membership value between community and node at the same time, with low time complexity and storage space. Experiments on real networks verify the correctness of the method, and prove that the method is suitable for large-scale network applications.

Agglomerative community detection on signed networks based on community adjacency list

With the development of data processing technology, complex network theory has been widely applied in many areas. Meanwhile, as one of the essential parts of network science, community detection is becoming more and more important for analyzing and visualizing the real world. Specially, signed network is a kind of graph which can more truly and efficiently reflect the reality, however, the study of community detection on signed network is still rare. In this paper, we propose a new agglomerative algorithm based on the modularity optimization for community detection on signed networks. The proposed model utilizes a new data structure called community adjacency list in signed (CALS) networks to improve the efficiency. Successive modularity computations make the connections between node changes so that the process time leads to substantial savings. Experiments on both real and artificial networks verify the accuracy and efficiency of this method, which is suitable for the application on large-scale networks.

A Backward Single Source Shortest Path Algorithm For Directed Graph

A new directed backward variant of the Single Source Shortest Path algorithm was described in this paper. This algorithm accept that approaching adjacency list of the given graph vertex loads showed up in expanding request. The running time of forward, based strategy algorithm is the best aftereffect of O (n), which are the most ideal forward-backward SSSP consequences of Wilson et al. Moreover, the likelihood of the new algorithm additionally requires O (n) time.This is an equally improved version of exponentially and polynomial small probability derived by Wilson et al.

Space-Efficient Fully Dynamic DFS in Undirected Graphs †

Depth-first search (DFS) is a well-known graph traversal algorithm and can be performed in O ( n + m ) time for a graph with n vertices and m edges. We consider the dynamic DFS problem, that is, to maintain a DFS tree of an undirected graph G under the condition that edges and vertices are gradually inserted into or deleted from G. We present an algorithm for this problem, which takes worst-case O ( m n · polylog ( n ) ) time per update and requires only ( 3 m + o ( m ) ) log n bits of space. This algorithm reduces the space usage of dynamic DFS algorithm to only 1.5 times as much space as that of the adjacency list of the graph. We also show applications of our dynamic DFS algorithm to dynamic connectivity, biconnectivity, and 2-edge-connectivity problems under vertex insertions and deletions.