This paper presents a method for discovering communities in graphs in linear time, O(V + E), by modeling the graph as an electric circuit. The method avoids edge cutting and uses voltage drops across networks to determine community structure. It allows for the efficient discovery of communities around a given node without extracting all communities from the graph. The algorithm is based on solving Kirchhoff equations, which can be computed in linear time by using a matrix inversion approach. The method is tested on real-world data, including the Zachary's karate club network and US college football data, demonstrating its effectiveness in identifying community structures. The algorithm is also applied to email data from HP labs, successfully identifying colleagues of a given node. The method is compared to other community detection algorithms, such as betweenness centrality, which are slower and more complex. While the method is efficient, it requires specifying the number of communities to identify, which may not always be known. The paper also discusses possible extensions of the method, including better statistical approaches and more sophisticated weight functions for community detection. The method is shown to be effective in identifying communities in large graphs and around individual nodes, making it a valuable tool for community discovery in complex networks.This paper presents a method for discovering communities in graphs in linear time, O(V + E), by modeling the graph as an electric circuit. The method avoids edge cutting and uses voltage drops across networks to determine community structure. It allows for the efficient discovery of communities around a given node without extracting all communities from the graph. The algorithm is based on solving Kirchhoff equations, which can be computed in linear time by using a matrix inversion approach. The method is tested on real-world data, including the Zachary's karate club network and US college football data, demonstrating its effectiveness in identifying community structures. The algorithm is also applied to email data from HP labs, successfully identifying colleagues of a given node. The method is compared to other community detection algorithms, such as betweenness centrality, which are slower and more complex. While the method is efficient, it requires specifying the number of communities to identify, which may not always be known. The paper also discusses possible extensions of the method, including better statistical approaches and more sophisticated weight functions for community detection. The method is shown to be effective in identifying communities in large graphs and around individual nodes, making it a valuable tool for community discovery in complex networks.