# 统计代写|网络分析代写Network Analysis代考|Discovering complexes by finding dense regions

## 统计代写|网络分析代写Network Analysis代考|Discovering complexes by finding dense regions

The molecular complex detection (MCODE) algorithm, described in the early work of Bader et al., [8], tries to find complexes by building clusters. The rationale of MCODE that takes in as input the interaction network, is the separation of dense regions based on an ad hoc defined local density. MCODE has three main steps: (i) node weighting, (ii) complexes prediction, (iii) post processing. In its first stage, MCODE weights all vertices based on their local network density. The local area, in which density is calculated, is delimited by an ad hoc defined subgraph structure called $k$-core. A $k$-core of a graph is the central most densely connected subgraph with minimal degree $k$. Thus the core-clustering coefficient of a vertex $v$ is the density of the highest $k$-core of the immediate neighborhood of $v$. Finally, the weight of a vertex is the product of the vertex core-clustering coefficient and the highest $k$-core level, $k m a x$, of the immediate neighborhood of the vertex.

The resulting weighted graph is given as input of the second stage. The algorithm, hence, starting from the highest weighted vertex, tries to span a region visiting vertices, whose weight is above a certain threshold, called vertex weight percentage (WWP). This step stops when no more vertices can be added to the complex, and it is repeated considering the next highest weighted network not already considered. Finally, the third step has to filter the complexes, which do not contain at least a $k$-core with $k=2$.

The third stage is the post-processing. Complexes are filtered if they do not contain at least a 2-core (graph of minimum degree equal to 2). The algorithm has two main options that determine the characteristics of this phase: fluff and haircut.

The algorithm has two modes of execution: a direct mode (in which the search starts from a given node), and an undirect mode (in which the seed is randomly selected). MCODE is freely available on the author’s website, ${ }^5$ and there also exists a version that runs as a plugin for the Cytoscape software.

## 统计代写|网络分析代写Network Analysis代考|Complex prediction via clustering

The paper of Ul-Amin et al. [2] presents another approach based on clustering an interaction network to find complexes. The rationale of the algorithm is the building of a cluster as a dense region embedded into a sparse region. The algorithm is logically organized in five major steps: (i) initialization, (ii) termination check, (iii) selection of starting node, (iv) cluster growth and (v) output.

In the first step, the algorithm takes as input an undirected graph and initializes its main variables: cluster density, cluster property, cluster ID. The algorithm calculates the minimum value of density for each generated cluster, i.e., the ratio of the number of edges present in the cluster and the maximum possible number of edges in the cluster. The cluster property $c p_{n, k}$ of any node $\mathrm{n}$, with respect to any cluster $k$ of density $d_k$ and size $\left|N_k\right|$, is the ratio between the total number of edges between the node $\mathrm{n}$ and each of the nodes of cluster and the product between the density and the size of the cluster $d_k$. The cluster ID, $k$ is initialized to 1.
In the second step, the algorithm verifies the termination conditions, and if the graph has no edges, the algorithm will end. Conversely, if the termination check fails, the algorithm in its third step, namely selection of starting node, will select a node as a starting point to build a new cluster. Hence, in the fourth step, namely cluster growth, the algorithm adds nodes to the cluster chosen from the neighbors of starting node. Neighbors are labeled with a priority in order to guide the cluster formation. Finally, when a cluster is generated, it is removed from the graph, and the clusterID, $k$ is incremented.

The algorithm is polynomial, and its complexity in the worst case is $O\left(N^3\right)$, where $N$ is the number of nodes. This complexity is due to the cost of sorting clusters.

# 网络分析代考

## 统计代写|网络分析代写Network Analysis代考|Discovering complexes by finding dense regions

Bader 等人 [8] 早期工作中描述的分子复合物检测 (MCODE) 算法试图通过构建簇来寻找复合物。将交互网络作为输入的 MCODE 的基本原理是基于临时定义的局部密度分离密集区域。MCODE 具有三个主要步骤：(i) 节点加权，(ii) 复合物预测，(iii) 后处理。在其第一阶段，MCODE 根据本地网络密度对所有顶点进行加权。计算密度的局部区域由一个名为k-核。Ak-图的核心是具有最小度的中心最密集连接的子图k. 因此顶点的核心聚类系数在是密度最高的k- 紧邻的核心在. 最后，顶点的权重是顶点核心聚类系数与最高点的乘积k-核心水平，k米AX, 顶点的直接邻域。

## 统计代写|网络分析代写Network Analysis代考|Complex prediction via clustering

Ul-Amin 等人的论文。[2] 提出了另一种基于聚类交互网络来寻找复合物的方法。该算法的基本原理是将集群构建为嵌入到稀疏区域中的密集区域。该算法在逻辑上分为五个主要步骤：（i）初始化，（ii）终止检查，（iii）起始节点的选择，（iv）集群增长和（v）输出。

myassignments-help数学代考价格说明

1、客户需提供物理代考的网址，相关账户，以及课程名称，Textbook等相关资料~客服会根据作业数量和持续时间给您定价~使收费透明，让您清楚的知道您的钱花在什么地方。

2、数学代写一般每篇报价约为600—1000rmb，费用根据持续时间、周作业量、成绩要求有所浮动(持续时间越长约便宜、周作业量越多约贵、成绩要求越高越贵)，报价后价格觉得合适，可以先付一周的款，我们帮你试做，满意后再继续，遇到Fail全额退款。

3、myassignments-help公司所有MATH作业代写服务支持付半款，全款，周付款，周付款一方面方便大家查阅自己的分数，一方面也方便大家资金周转，注意:每周固定周一时先预付下周的定金，不付定金不予继续做。物理代写一次性付清打9.5折。

Math作业代写、数学代写常见问题

myassignments-help擅长领域包含但不是全部: