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Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, "Bat-Cluster (BC)." This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values.

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Graph Visualization is a technique that helps users to easily comprehend connected data (social networks, semantic networks, etc.) based on human perception. With the prevalence of Big Data, these graphs tend to be too large to decipher by the user's visual abilities alone. One of the leading causes of this problem is when the nodes leave the visualization space. Many attempts have been made to optimize large graph visualization, but they all have limitations. Among these attempts, the most famous one is the Force Directed Placement Algorithm. This algorithm can provide beautiful visualizations for small to medium graphs, but when it comes to larger graphs it fails to keep some independent nodes or even subgraphs inside the visualization space. In this paper, we present an algorithm that we have named "Forced Force Directed Placement". This algorithm provides an enhancement of the classical Force Directed Placement algorithm by proposing a stronger force function. The "FForce", as we have named it, can bring related nodes closer to each other before reaching an equilibrium position. This helped us gain more display space and that gave us the possibility to visualize larger graphs.

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