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In order to predict software defects, this paper proposes a software defect prediction method based on complex network analysis. Most of the existing evaluation methods are based on undirected, unweighted network, failing to reflect the real situation of complex software. First, method proposed in this paper abstracts software system as directed weighted network on the granularity of class. Then, based on PageRank algorithm, this paper proposes KeyNodeRank algorithm to calculate node importance in global network. Node importance can be used to predict defects of software system. Experimental results show that the proposed method has a higher accuracy in predicting software defect. It has important significance for locating software defects, testing, improving software quality and software maintenance.

In this chapter, an introductory analysis of human intelligence and consciousness is executed to establish a conceptual foundation for the intelligent organization theory. Fundamentally, the new intelligence mindset and thinking, and intelligence paradigm focus on high intelligence/ consciousness-centricity. It concentrates on human intrinsic intelligence/ consciousness sources (its intense intelligence and consciousness — awareness and mindfulness), and stipulates that organizing around intelligence (a strategic component of the complexity-intelligence strategy) is the new strategic direction to be adopted by all human organizations in the present knowledge-intensive, fast changing, and not always predictable environment (limited predictability). In addition, the characteristics and variation in capabilities of intelligence and consciousness are further scrutinized using an intelligence spectrum (compared to other biological intelligence sources on this planet — encompassing proto-intelligence, basic life-intelligence, basic human intelligence and advanced human intelligence). In the intelligent organization theory, consciousness (awareness, mindfulness) only exists in the living/biological world, and mindfulness is confined to humanity.

Subsequently, intelligence/consciousness management and its associated dynamics that are critical activities of intelligent human organization (iCAS) are more deeply examined with respect to complexity-centricity (encompassing attributes such as stability-centricity, autopoiesis, symbiosis, self-centric, network-centric, org-centric, independency, interdependency, intelligence-intelligence linkage, engagement, self-organization/ self-transcending constructions, local space, complex networks, constructionist hypothesis and emergence). Concurrently, the urgency and impact for nurturing the new intelligence mindset and intelligence paradigm is also discussed. In a situation with escalating complexity density, this paradigmatic shift in mindset and thinking in leadership, governance and management of human organizations is highly significant for higher functionality and coherency — to all human interacting agents (both leaders and followers), as well as the organizations themselves.

Another component of the complexity-intelligence strategy examined is the nurturing of an intelligent biotic and complex adaptive macro-structure that will serve all human organizations better (towards higher coherency, synergy and structural capacity). The analysis clearly indicates the necessity of systemic transformation or structural reform that is more coherent with intelligence/consciousness, and information processing and knowledge acquisition capability. In this case, a greater operational/ practical utility and higher structural capacity can be achieved with the presence of the intelligent biotic macro-structure and agent-agent/ system micro-structure (principle of locality) that concurrently supports the intelligent complex adaptive dynamic (iCAD) better — a finer synchrony between structure and dynamic — higher intelligence advantage.

The inherent micro-structure (agent-agent/system) of human organizations has been introduced in Chapters 2 and 3. Fundamentally, human organizations are composite complex adaptive systems with human beings as interacting agents (each an intrinsic complex adaptive system). This chapter further analyzes the basic conceptual foundation of the multi-layer structure, including advantages of the intelligent biotic macro-structure (with inherent features similar to that of an intelligent biological being — a structural reform), and its unique and more integrative complex adaptive dynamic in intelligent human organizations (towards iCAD). The necessity of nurturing an intelligent biotic macro-structure with vital characteristics that better synchronize and enhance sophisticated information/knowledge-related activities is highly beneficial — achieving a higher structural capacity. Thus, the attributes, functions and higher structural capacity of the more intelligent biotic macro-structure can reinforced the competitiveness of any categories of human organizations extensively.

In this respect, connecting and engaging of intelligence/consciousness sources (individual and collective), organizing around intelligence, intelligence/ consciousness management, and the intelligent biotic macrostructure are mutually enhancing (towards higher coherency). Apparently, being intelligence/consciousness-centric is a beneficial and critical activator (strategic foundation) of the intelligence paradigmatic shift. In the present context, the roles and integration of intelligence, information and knowledge, as well as nurturing a ‘common’ language and elevating coherency in human organizations (with respect to the macro-structure and micro-structure, as well as their higher collectiveness — collectiveness capacity) must be more deeply scrutinized and utilized. The presence and significance of the individual intelligence enhancer encompassing three entities namely, intelligence, knowledge, and theory in the human thinking systems, and the necessity of nurturing a similar and effective intelligence enhancer at organizational level are analyzed. Subsequently, the supporting roles and contributions of artificial intelligence systems are also examined.

In between the macro-structure and micro-structure are two meso-structures. In the intelligent organization theory, the complexity meso-structure encompasses spaces of complexity and punctuation points. In this respect, complexity is a highly significant focal point, and the exploitation of co-existence of order and complexity is a new necessity (complexity-centricity). Next, the network meso-structure encompassing complex network (network of networks) is also an inherent structure and dynamic in all human organizations. This meso-structure is briefly introduced, and will be more deeply analyzed with respect to governance (network-centricity, network governance).

Hence, it is crucial to lead and manage human organizations with a strategic approach that integrates the above multi-layer structure/ dynamic at all time so that a higher structural capacity, collectiveness capacity, adaptive capacity, self-organizing capacity, and emergence-intelligence capacity can be nurtured. In the current highly competitive context, possessing these positive capabilities to elevate coherency and synergetic characteristics (including social consensus and the construal aspect) and dynamic is also highly crucial — a key focus of the complexity-intelligence strategy (towards achieving higher organizational mental cohesion). Hence, the significance and impact of nurturing intelligent human organizations with the complexity-intelligence-centric and network-centric approach that leads to the emergent of smarter evolvers and emergent strategists must be better understood and adopted. (The conceptual foundation on structural-dynamic coherency and synergy in intelligent human organizations developed in this chapter will be more deeply reviewed and exploited in later chapters.)

The first section of this chapter is an introduction to relativistic complexity (a significant component of the intelligent organization theory). The presence of intense intelligence/consciousness-centricity and 3^rd order stability-centricity in the human world renders complexity relativistic. The impact of the human mental space is so tremendous that complexity is in the mind of the beholder, and predictability becomes highly subjective. In this situation, the state of relativistic static equilibrium may be beneficial. Certain spaces of complexity appear as spaces of relativistic order with surface patterns becoming more apparent. Such spaces must be creatively explored and exploited (higher exploratory capacity) leading to a more advanced level of intelligence advantage. In this respect, effective self-transcending constructions, high self-organizing capacity and emergence-intelligence capacity are significant attributes that the new leadership and governance system in intelligent human organizations must exploit. Holistically, the two strategies focus on concurrent exploitation of intelligence/consciousness-centricity and relative complexity, and optimizing the more comprehensive contributions of the integrated deliberate and emergent strategy.

Many issues/problems that present human organizations (nations, political systems, communities, business organizations, markets) are encountering due to accelerating changes (mindset, thinking, values, perceptions, expectations, redefined boundaries and high interactive dynamics) that cannot be well-managed with traditional knowledge and hierarchical practices are affecting governance and governance systems. Fundamentally, governance deals with power, interest, and conflict. The traditional governance systems are hierarchical, highly directed, controlled and managed, and the relational aspect has not been allocated sufficient priority resulting in extensive disparities. In the current complex dynamical and high interdependency environment, its weaknesses and constraints are highly apparent. The latter includes ‘space-time compression’; incoherency in thinking, values, perceptions, and expectations between the leadership and the other agents; diversification in stakeholders’ needs not accommodated; and constraints of current governance theories. Thus, a new theory that provides a more ‘realistic’ foundation is essential for deeper contemplation.

Primarily, recognizing the inherent strengths of human agents and the fundamental constraints/weaknesses of human organizations is a key foundation towards better adaptation, leadership, governance, resilience and sustainability. In all human organizations, the agents are intrinsic intense intelligence/consciousness sources that could easily transform their behavioral schemata. This observation contradicts the Newtonian/design paradigm, as the organizational dynamic of human agents is complex, nonlinear, constantly/continuously changing with limited predictability. In addition, human agents are self-centric, self-powered, stability-centric, independent and interdependent, network-centric and self-organizing due to high awareness. In this situation, high self-organizing capacity and emergence-intelligence capacity are new niches. However, this phenomenon can create new opportunities, innovation, and elevates competiveness; or destruction.

In particular, effective leadership and governance are spontaneously emerging key requirements in all human groupings — a primary trait for human collective survival. Historically, many organizations disintegrated because of the weaknesses in leadership and governance. Currently, with more knowledge-intensive and higher participative new agents (self-powered intrinsic leadership) possessing modified attributes that are dissimilar from the older generations (also due to the deeper integration of the economic, social, political, and environmental perspective), reduces consensus and collaboration, and renders governance and leadership even more nonlinear or dysfunctional. In particular, the traditional governance systems of more organizations are manifesting their constraints and incompetency, including incoherency due to new values and cultural pressure, and the wider spread of self-organizing networks. The emergent of informal networks is a more commonly observed phenomenon worldwide. Apparently, a deeper comprehension on the diminishing effect of the traditional organizational thinking (political, social, economic), governance capacity, precise strategic planning, decision making, hierarchical structure, communications and engagement, empowerment leadership, management, operations, and the highly nonlinear relational parameter is essential. Apparently, new principles of governance must emerge (intelligent human organization > thinking system + feeling system).

The new paradigmatic path of the intelligence governance strategy that exploits intelligence/consciousness-centricity, complexity-centricity, and network-centricity concurrently, introduces a new basic strategic path towards better adaptive governance and acceptance governance. The latter focuses on integrating self-powered self-organizing governance, reducing direct governance, and increasing e-governance and network-centric governance as a new necessity. In this case, the merits of adopting the intelligence leadership strategic approach simultaneously are more apparent. Hence, the new governance focal points must include more and better interconnected actors, the critical ability of self-organizing communications (supported by mobile/social media development), immersion of leadership nodes in networks (better exploitation of e-governance), increasing coherency of complex networks (exploiting interdependency of network of networks, and better network management), and elevating self-transcending constructions capability (higher self-organizing governance capacity and emergence-intelligence capacity) that better facilitates emergence through multi-level and ‘multi-lateral’ dynamics (complex adaptive networks <=> intelligent networks). Thus, the intelligence governance strategy emphasizes that mass lateral collectivity (acceptance governance) rather than selective enforced hierarchical empowerment as the more constructive approach in the present contact. In particular, the stabilityinducing role of leaders and institutions are critical. Apparently, optimizing the ‘everybody is in charge’ phenomenon (whenever necessary) is a more viable option.

Modern system has become increasingly complex to design and build. Once a system has been built, it is also difficult to detect if it is a stabilizing when some error happened. Predict the cascading failure and decrease the probability of it is a central issue in the research of complex network. Predicting the cascading failure before it happens is still a popular issue. The paper has made a model to describe the cascading failure base on a classical queuing theory model M^[k]/M/1 to compare with the model Coupled Map Lattices (CML). The proposed model not only can describe the process and the scale of the failure as CML, but it can also give a prediction for the failure. It serves as a useful guide before building a complex system. It can also give a standard for the stability of the proposed network.

This paper provides a partial summary of our recent work on propagation dynamics of complex networks, mainly on constructing and studying network models of disease spreading and related propagation problems. Traditional compartmental models of disease spreading categorize individuals from a population based on their current pathology. These methods provide a population-based description that offers a smooth continuous and exponential response to the presence of an infectious agent. In many cases the available data is inconsistent with the standard models of disease spreading and can be more readily explained using a discrete agent-based model of spreading on complex networks. Moreover, models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can be sorted into different classes according to their infectivity. In addition, some diseases may develop through several stages, or with mobility property, or with mutually exclusive feature (multi-strain epidemics). So in this paper, in order to better study the dynamical behavior of epidemics, we discuss different epidemic models on complex networks, and provide a mathematical analysis of the epidemic dynamics and spreading behavior, obtaining the epidemic threshold for each case. Some other related diffusion and propagation processes, such as information transmission dynamics, traffic flows, contact processes, etc., are also briefly discussed.

With the discovery of the small-world and scale-free networks, the researchers in physics, biology, mathematics and computer science dedicate to the study of complex networks. The reason for this increased attention is that complex networks can describe and characterize many natural complex systems. Among the issues of complex networks, synchronization and its control inside a network has attracted considerable interest. Recently, outer synchronization was introduced to study the dynamics between two coupled networks. This chapter mainly makes a presentation about the outer synchronization and its control between two networks with (or without) time delays. Firstly, anti-synchronization between two networks with two special interactions is presented. In addition, anti-synchronization for the two networks with delayed coupling is investigated via the pinning control technique. Finally, generalized synchronization between two coupled complex networks is studied. Numerical examples are given to show the efficiency of the obtained theoretical results.

The “non-consensus” opinions pervade all patterns of human's interactive activities. In this paper, we study the phenomenon of the information competition dynamics induced by messages with opposite meaning. We construct a new information competition model which could perfectly match the reality in complex networks. It is shown that the k-shell value of nodes plays an important role in reflecting its competitive power in the information competition processes, classifying the nodes in the network with the k-shell decomposition technique. In particular, by varying the variables in our model different cases of information competition phenomena can be successfully explained. Our findings indicate that this new information competition model should be useful to the study on network information dissemination.