【免费】图与网络算法优秀论文１２1资源-CSDN文库

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标题中的“图与网络算法优秀论文121”暗示了这篇研究聚焦于使用图论和网络分析方法来解决特定问题，而描述中的“Human Capital Dynamic Network Model of Team Science”则进一步明确了研究的具体领域：团队科学中的人力资本动态网络模型。这篇论文的主要目标是探索如何在组织中有效地管理人力资本，以提升团队的生产力。文章构建了一个静态的人力资本网络。在这个网络中，ICM的370个职位被用作节点，节点间的联系——包括隶属关系和合作关系——作为边。通过设定不同的节点（职位）的量化值来反映其基本属性，如工作职责、技能需求等。通过建立职位档案矩阵（Post Archives Matrix, PAM），可以记录每个职位的内部特性，这有助于模拟真实情况。每条边的权重表示两个节点之间是否存在工作关系，权重为1表示存在合作关系。作者提出了动态网络模型，以涵盖员工离职、晋升和招聘等导致公司内部变化的因素。当网络发生变化时，考虑了员工工作热情的下降和周围员工离职率的上升。离职算法分为低层级员工可直接离职，以及中层员工离职需要通过晋升和招聘填补空缺两种情况。晋升算法设定了不同层级的晋升条件，确保内部员工的逐级晋升，并且优先考虑内部晋升。招聘算法则将空缺职位的2/3视为需要外部招聘，以保持团队的多样性和新鲜血液。此外，论文可能还探讨了如何利用关系紧密度计算方法来量化团队绩效，结合隶属关系和合作关系，这为评估团队整体表现提供了新的视角。这些网络模型和算法的应用对于理解团队动态、优化人力资源配置、预测团队稳定性以及提升组织效率具有重要意义。这篇论文通过构建和分析人力资本的静态和动态网络模型，深入研究了团队科学中的关键问题，即如何管理和优化人力资源以提升团队生产力。它引入了量化指标、动态演化规则以及性能评估方法，为组织决策者提供了有力的理论工具。

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Human Capital Dynamic Network Model of Team Science

Summary

As an indispensable part of team science, managing human capital in organizations directly determines

the company’s productivity. This paper tries to combine network model with team science.

Firstly, build the human capital static network: we use ICM’s 370 positions as nodes, the affiliations and

cooperative relationships as edges. For different nodes of position, we define some quantified values that

can reflect basic attributes of the position based on proper assumptions. By building the Post Archives

Matrix(PAM), we can record the internal attributes of each position. This matrix can be really helpful to

simulate the real conditions. For every edge (, ), the value is 1 if and only the node  and node  have

the working relationship. Then we put forward the relationship compactness calculation method, which

combines affiliation and cooperative relationship, and this is applied to quantify team performance.

Secondly, build the human capital dynamic network model: we regard employees’ demission, promotion

and recruitment as the main causes of network evolution and internal churn in the company. Based on the

real conditions and some reasonable assumptions, we designed three evolution driving algorithms. For

demission algorithm, there are two conditions: if the employee who wants to leave is in low level, he

can dismiss directly; if he is in middle level, promotion and recruitment algorithm must be called to fill

the position. What’s more, we consider carefully about the declination of working enthusiasm and the

increasing in churn rate for surrounding people when there is one employee churn. For promotion

algorithm, we determine the promotion conditions for different level to realize the layer-by-layer

promotion of internal employees. Besides, promotion is always in priority to recruitment. For

recruitment algorithm, we regard the 2/3 of the vacant position as recruitment plan and we build the

on-position time vector to represent delaying effects.

Thirdly, focusing on the issues that the supervisor needs us to consider, we draw the conclusions: in the

next two years, the recruitment and training cost are 26.22σ and 140.15σ; When the churn rate reaches

25% and 35%, working rate is steadily below 80% and bring some negative effects such as the declining

of company performance and employees’ enthusiasm; The no-external recruitment policy is beneficial to

maintain the company performance and working rate, but we still have to admit that it will cause the

declining of enthusiasm of middle-level employees.

Finally, we regard friendships, competition and Human Capital as three network layers to describe the

connection compactness in the whole team. By using the main statistical indicators (such as the average

length of path), we draw the conclusion that multilayer network can reflect internal connections more

precisely and comprehensively than human capital network.

Contents

1 Introduction 1

1.1 Problem analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.3 Terminology and deﬁnitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.4 Our work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Human Capital Evolution Network Model 3

2.1 Model overview and concepts deﬁnition . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Human capital static network model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2.1 The main assumptions of static human capital network model . . . . . . . . . . 4

2.2.2 The deﬁnition of node and edge . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2.3 Node attributes in the network . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.4 The input and initialization of static network model. . . . . . . . . . . . . . . . 6

2.2.5 Output of static network model . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3 Three evolution driving algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3.1 Employee dismission algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.3.2 Employee promotion algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.3 Employee recruitment algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4 Simulation results of the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4.1 Prediction for ICM’s budget requirements . . . . . . . . . . . . . . . . . . . . . 10

2.4.2 Effects of the changing churn rate . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.4.3 Effects of no external recruitment of middle-level employees . . . . . . . . . . . 13

3 Multilayer Network Model 16

3.1 The multilayer network for ICM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2 The analysis of multilayer network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4 Sensitivity Analysis 17

4.1 Uncertain effects produced by stochastic simulation . . . . . . . . . . . . . . . . . . . . 18

4.2 Subjective effects produced by some coefﬁcients . . . . . . . . . . . . . . . . . . . . . 18

5 Conclusion 18

5.1 Strengths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5.2 Weaknesses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Team # 35778 Page 1 of 20

1 Introduction

Human capital is an intangible asset, it is relatively abstract and it’s meaning develops with the devel-

opment of our society. Pierre Bourdieu offers a nuanced conceptual alternative to human capital that

includes cultural capital, social capital, economic capital, and symbolic capital

[1]

. Human capital, when

viewed from a time perspective, consumes time in one of key activities:

• Knowledge (activities involving one employee);

• Collaboration (activities involving more than 1 employee);

• Processes (activities speciﬁcally focused on the knowledge and collaborative activities generated

by organizational structure-such as silo impacts, internal politics, etc.);

• Absence(annual leave, sick leave, holidays,etc.).

As the complexity of the workplace continues to grow, organizations increasingly depend on teams.

Building an organization ﬁlled with good, talented, well-trained people is one of the keys to success.

More and more people gradually begin to realize its charming power. So it is extremely important

for the HR ofﬁce to assign employee to positions appropriate to their talents and experience. These

positions are just the efﬁcient communication systems to facilitate development of innovative ideas and

quality products (commodities or services).But how to manage the human capital has always been a great

challenge.

1.1 Problem analysis

We focus on a more practical problem on the Information Cooperative Manufacturing (ICM) organiza-

tion of 370 people, which is in a highly competitive market place.

Now, the actual situation of the organization: only 85% of its 370 positions are ﬁlled at any time. Con-

sidering different position layers in which people are required to have certain years of experience, the

low quality employees often stay with the company for a full career. However, mid-level positions suffer

much higher turnover, and it’s often the case that churn seems to diffuse from former employees to oth-

ers, so they are critical ones when consider the position changes. As for the CEO, the ratio of their salary

to worker is approximately 10 times. Besides, in terms of time, the earlier an employee gains the loyalty,

the more productive is the organization. What’s more, the annual evaluation based on performance is

judged by the supervisor rather than the HR ofﬁce.

Our tasks:

• Build a human capital network model of ICM organization’s personnel situation that can identify

dynamic processes to get certain inﬂuence such as the cost, the direct and indirect effects on

organization’s productively with a certain churn rate as well as simulate or predict what will happen

with a changed churn rate.

• Build a multilayer ncetwork that can connect our Human Capital network to other organizational

network layers such as information ﬂow, trust, inﬂuence, and friendship.

1.2 Literature review

"Human capital" has been and continues to be criticized in numerous ways. Many researchers have en-

gaged in deﬁning and developing this concept

[2] [3]

. In the early days, it is an aggregate economic view of

the human being acting within economies, which is an attempt to capture the social, biological, cultur-

al and psychological complexity as they interact in explicit and/or economic transactions. Today, most

theories attempt to break down human capital into one or more components for analysis

[4]

-usually called

Team # 35778 Page 2 of 20

"intangibles". Accordingly much more attention is paid to factors that led to success v ersus f ailure where

human management is concerned. The role of leadership, talent, even celebrity is explored. How to

make the best use of human capital in the organization, more speciﬁcally, to retain good people, keep

them properly trained and placed in proper positions, and eventually target new hires to replace those

leaving the organization has always been a challenge work.

Network has been widely used in various aspects, especially in natural science. For example, biolog-

ical networks provide a mathematical analysis of connections found in ecological, evolutionary, and

physiological studies, such as neural networks

[5]

. In computer networks, networked computing devices

pass data to each other along data connections (network links). As more and more people realized the

power of the network, other complex networks, such as social network and business network forms the

nascent ﬁeld of network science

[6]

. For instance, a social network is a social structure made up of a set

of social actors (such as individuals or organizations) and a set of the dyadic ties between these actors.

Since using network in social areas is newly developed, the theory is relatively scarce and many theories

are more focused on how to establish the structure of the network rather than the function-the practical

use. So it is a novel perspective to build a human capital network to study how the churn and recruit

employee will affect the efﬁciency of the organization. And as teams have increasingly become a way

of life in many organizations, using the new concept of team science to build a human network has little

been discussed.

What’s more, our life is ﬁlled with all kinds of networks, if we do more research on these networks, we

may ﬁnd connection between them, especially a set of entities interact with each other in complicated pat-

terns that can encompass multiple types of relationships,and it is important to take such"multilayer"features

into account to try to improve our understanding of complex systems. So connect our Human Capital

network to other organizational network layers such as information ﬂow, trust, inﬂuence, and friendship

is just our creative and challenging work.

1.3 Terminology and deﬁnitions

• Churn: the resulting turbulence when people leaving for other jobs or retiring are replaced.

• Churn rate(turnover rate):a measure of the number of individuals or items moving out of a col-

lective group over a speciﬁc period of time,and it can be deﬁned as follows:

(%) =

+ N

)

× 100%

where, N

is the number of employees resigned during the month, N

is the number of employees

at the start of the month and N

is the total number of employees at the end of the month.

1.4 Our work

This paper tries to combine network model with team science to deal with management issues.In section

2, we build human capital network. Firstly, in part 2 we use ICM’s 370 positions as nodes, the afﬁliations

and cooperative relationships as edges to build a human capital static network model. Then in part 3 we

design three evolution driving algorithms respectively which make the network change with certain initial

conditions as well as the development of algorithm in time dimension. Thirdly, focused on the issues

that the supervisor need us to deal with, we revise our model’s initial parameters and algorithms. Finally

,in part 4 ,based on our simulation results, we solve the issues supervisor requires and analyze effects

under different conditions. In section 3, after studying the theory of multilayer network, we regard

friendships, competition and afﬁliation as three network layers to describe the connection compactness in

the whole team. We also ﬁnd that multilayer network can reﬂect internal connections more precisely and

comprehensively than human capital network. In section 4, we do the sensitivity analysis for simulation

Team # 35778 Page 3 of 20

methods and some parameters. In section 5, we draw the conclusion of our model and get our strength

and weakness.

2 Human Capital Evolution Network Model

2.1 Model overview and concepts deﬁnition

From the perspective of the human resources department, we build the human capital network model to

reﬂect the human capital as well as its changes of each level of employees. According to the require-

ments, our network should have the following functions:

• The network can clearly show the distribution of different levels of employees in each divisions or

ofﬁces as well as the internal leading and being led relationships, which is the basic structure of

the promotion mechanism in the organization.

• Since it is a human capital network, we must pay more attention to the main factors that can affect

the human capital, which includes: the ability an employee has had when he gets the position,

the productive enthusiasm of the employee, the time an employee stays in certain position and the

training he obtained.

• Our human capital network is an evolutive network model. It has three fundamental dynamic

phenomena: employees’ internal promotion, employees’ demission and external recruitment.

The churn of the employee is especially worrying in ICM organization that the current churn rate is

18% per year. So we should emphatically consider the how the churn rate will directly and indirectly

affect the total beneﬁt and working rate (the effects on employees’ relationship and enthusiasm will also

be considered). To better illustrate our human capital evolution network, the important elements and

concepts are deﬁned as follows:

• Node: The nodes in the network model refer to 370 positions set up in the company. This is

quite different from the nodes in a normal social network, which represents the actual person. In

order to show the characteristic of these nodes more clearly, we build a matrix with 370 rows, and

we call it "Post Archives Matrix" (denoted as PAM for the following illustration). In the matrix,

each line represents a quantitative attribute of certain position. In order to satisﬁed the function,

the quantitative attributes contains the level of position, the on-the-job condition of the position,

working time and which division or ofﬁce it belongs to.

• Edge: Edge is the ditch that two points interact with each other. Focused on the present network

that regard position as points, the two most direct connections are the afﬁliation in the division or

the ofﬁce and the leading and being led relationship between divisions. In order to reﬂect inﬂuence

of individual jobs on team work, we establish the matrix with the size of 370 × 370 to reﬂect the

afﬁliation of different positions, which will be analyzed in the following part of this paper about

the discussion of the teamwork.

• Factor: Based on the description of the problem and the reference given

[7]

,the input factors of the

evolution network model are the on-job-rate at the initial time and the churn rate, the output factors

are the on-job-rate in the future , corporate performance, team work, the costs of recruitment and

training , etc.

• Corporate performance: Our company’s corporate performance is the summation of productiv-

ity in each position. In order to calculate changing performance, we consider that the maximum

productivity is the summation of basic productivity in certain position and the individual experi-

ence. Moreover, the actual productivity is on the base of the maximum productivity and effected

by the individual working enthusiasm as well as team work, which will be illustrated in detail in

our model.

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