Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.

https://arxiv.org/pdf/cond-mat/9910332.pdf

Emergence of Scaling in Random Networks

In this paper, we argue that the ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends is critical to its innovative capabilities. We label this capability a firm's absorptive capacity and suggest that it is largely a function of the firm's level of prior related knowledge. The discussion focuses first on the cognitive basis for an individual's absorptive capacity including, in particular, prior related knowledge and diversity of background. We then characterize the factors that influence absorptive capacity at the organizational level, how an organization's absorptive capacity differs from that of its individual members, and the role of diversity of expertise within an organization. We argue that the development of absorptive capacity, and, in turn, innovative performance are history- or path-dependent and argue how lack of investment in an area of expertise early on may foreclose the future development of a technical capability in that area. We formulate a model of firm investment in research and development (R&D), in which R&D contributes to a firm's absorptive capacity, and test predictions relating a firm's investment in R&D to the knowledge underlying technical change within an industry. Discussion focuses on the implications of absorptive capacity for the analysis of other related innovative activities, including basic research, the adoption and diffusion of innovations, and decisions to participate in cooperative R&D ventures. **

/pdf/absorptive-capacity-a-new-perspective-on-learning-and-1n1c9gg9x0.pdf

Absorptive capacity: a new perspective on learning and innovation

The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. 
Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. 
The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. 
The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

/pdf/statistical-mechanics-of-complex-networks-1zfjpvxipu.pdf

Statistical mechanics of complex networks

The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.

The PageRank Citation Ranking : Bringing Order to the Web

The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of context on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authorative” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristrics for link-based analysis.

/pdf/authoritative-sources-in-a-hyperlinked-environment-1p23r698i9.pdf

Authoritative sources in a hyperlinked environment

Retweeting is the key mechanism for information diffusion in Twitter. It emerged as a simple yet powerful way of disseminating information in the Twitter social network. Even though a lot of information is shared in Twitter, little is known yet about how and why certain information spreads more widely than others. In this paper, we examine a number of features that might affect retweetability of tweets. We gathered content and contextual features from 74M tweets and used this data set to identify factors that are significantly associated with retweet rate. We also built a predictive retweet model. We found that, amongst content features, URLs and hashtags have strong relationships with retweetability. Amongst contextual features, the number of followers and followees as well as the age of the account seem to affect retweetability, while, interestingly, the number of past tweets does not predict retweetability of a user's tweet. We believe that this research would inform the design of sensemaking and analytics tools for social media streams.

/pdf/want-to-be-retweeted-large-scale-analytics-on-factors-3d0ov0dmc7.pdf

Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network

We present a new focus+context (fisheye) technique for visualizing and manipulating large hierarchies. Our technique assigns more display space to a portion of the hierarchy while still embedding it in the context of the entire hierarchy. The essence of this scheme is to lay out the hierarchy in a uniform way on a hyperbolic plane an d map this plane onto a circular display region. This supports a smooth blending between focus and context, as well as continuous redirection of the focus. We have developed effective procedures for manipulating the focus using pointer clicks as well as interactive dragging, and for smoothly an imating transitions across such manipulation. A laboratory experiment comparing the hyperbolic browser with a conventional hierarchy browser was conducted.

/pdf/a-focus-context-technique-based-on-hyperbolic-geometry-for-f0ntnof99s.pdf

A focus+context technique based on hyperbolic geometry for visualizing large hierarchies

A system for controlling the distribution and use of digital works using digital tickets. In the present invention, a “digital ticket” is used to entitle the ticket holder to exercise some usage right with respect to a digital work. Usage rights are used to define how a digital work may be used or distributed. Each usage right may specify a digital ticket which must be present before the right may be exercised. Digital works are stored in repositories which enforce a digital works usage rights. Each repository has a “generic ticket agent” which punches tickets. In some instances only the generic ticket agent is necessary. In other instances, punching by a “special ticket agent” residing on another repository may be needed.

System for controlling the distribution and use of digital works using digital tickets

A fee accounting mechanism for reporting fees associated with the distribution and use of digital works. Usage rights and fees are attached to digital works. The usage rights define how the digital work may be used or further distributed. Usage fees are specified as part of a usage right. The digital works and their usage rights and fees are stored in repositories (201). The repositories control access to the digital works. Upon determination that the exercise of a usage right requires a fee, the repository generates a fee reporting transaction (302). Fee reporting is done to a credit server (301). The credit server collects the fee information and periodically transmits it to a billing clearinghouse (303).

System for controlling the distribution and use of digital works having a fee reporting mechanism

One of the most common modes of accessing information in the World Wide Web is surfing from one document to another along hyperlinks. Several large empirical studies have revealed common patterns of surfing behavior. A model that assumes that users make a sequence of decisions to proceed to another page, continuing as long as the value of the current page exceeds some threshold, yields the probability distribution for the number of pages that a user visits within a given Web site. This model was verified by comparing its predictions with detailed measurements of surfing patterns. The model also explains the observed Zipf-like distributions in page hits observed at Web sites.

/pdf/strong-regularities-in-world-wide-web-surfing-2jykkoofge.pdf

Peter Pirolli

Papers

Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network

A focus+context technique based on hyperbolic geometry for visualizing large hierarchies

System for controlling the distribution and use of digital works using digital tickets

System for controlling the distribution and use of digital works having a fee reporting mechanism

Strong Regularities in World Wide Web Surfing