# A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes
## 1 Introduction  
The detection of protein complexes in Protein-Protein Interaction (PPI) networks is an essential task in system biology for deciphering the cellular organization and functional mechanism. Protein complexes perform the majority of a cell's functional actions [1; 2; 3]. As a result, detecting protein complexes is a critical research topic in systems biology. Understanding biological processes is also important in a variety of cytoplasmic systems and helps in the diagnosis of complex diseases [4; 5; 6]. Though there are numerous laboratory techniques for detecting protein complexes, most of them tend to be expensive and time-consuming. This has led to the use of computational methods as an efficient approach to detect protein complexes [7]. Computational methods for protein complex detection are generally classified into two broad classes depending on the information required during the complex detection procedure [8]. The first class is known as a topology-based approach, which just uses PPI network topological information to detect protein complexes. The second class uses both topological and biological data to detect protein complexes such as DPC [9], GMTP [10], and IPC-BSS [11]. Recently, a number of topology-based approaches have been developed to detect protein complexes. For example, there is k-cliques or cliques-based method such as CMC [12] and CFinder [13]; Sub-network density-based methods such as MCL [14; 15; 16], DPClus [17; 18],and SPICi [19]; modularity-based method such as CALM [20] and ClusterONE [21]; core-attachment structure-based methods such as COACH [22]and Core [23] and rank and spoke-based methods such as ProRank+ [24]. Nevertheless, these topological-based methods do not identify the state and structure of protein complexes in a PPI network. For instance, CFinder [13], detects protein complexes based on the clique percolation method (CPM) [25], an approach which is computationally expensive when handling large-scale PPI networks due to the NP-complete problem that requires protein complex to be k-clique [26; 27]. Related studies have also applied a sub-network density-based approach such as Markov Clustering (MCL) [15; 16], and tend to detect protein complexes based on the interaction of proteins within a sub-network (protein complex) in a random walks fashion [7; 8; 28; 29]. Moreover, a heuristic network clustering technique such as SPICi [19], has shown to be efficient for detecting protein complexes based on the local density and support measure. However, this technique is often unreliable when it comes to the detection of protein complexes with overlapping structures especially with high functional similarity. This has led to the development of DPClus [17] as an efficient method for detecting overlapping protein complexes such as these. However, methods such as ClusterONE that utilize MMR for overlapping complex detection tend to miss some attachment proteins, which could result in false positives for protein complex detection [18; 20]. Filtering methods such as ProRank+ [24]and PEWCC [30] have been adopted to increase the reliability of PPI networks. Recently modularity-based clustering techniques such as PCR-FR [31], CALM [20], ClusterONE [21] and EPOF [32] have been proposed for detecting protein complexes in densely and sparsely connected network structures [13; 33; 34; 35; 36; 37; 38]. Generally, the core of a protein complex is frequently a dense sub-network with attachment proteins that are closely linked to the complex's core proteins which help these proteins perform auxiliary functions [22]. Protein complexes have an inherent organization and a common architecture [39; 40]. Several techniques for identifying protein complex cores based on core-attachment structure have been investigated to this point, including COACH [22], and Core [23]. Another popular technique that has been used in the detection of protein complex cores is the co-attachment method which is often based on the network core-network structure [41; 42; 43].