Due to the ubiquity of time series with long-range correlation in many areas of science and engineering, analysis and modeling of such data is an important problem. While the field seems to be mature, three major issues have not been satisfactorily resolved. ͑i͒ Many methods have been proposed to assess long-range correlation in time series. Under what circumstances do they yield consistent results? ͑ii͒ The mathematical theory of long-range correlation concerns the behavior of the correlation of the time series for very large times. A measured time series is finite, however. How can we relate the fractal scaling break at a specific time scale to important parameters of the data? ͑iii͒ An important technique in assessing long-range correlation in a time series is to construct a random walk process from the data, under the assumption that the data are like a stationary noise process. Due to the difficulty in determining whether a time series is stationary or not, however, one cannot be 100% sure whether the data should be treated as a noise or a random walk process. Is there any penalty if the data are interpreted as a noise process while in fact they are a random walk process, and vice versa? In this paper, we seek to gain important insights into these issues by examining three model systems, the autoregressive process of order 1, on-off intermittency, and Lévy motions, and considering an important engineering problem, target detection within sea-clutter radar returns. We also provide a few rules of thumb to safeguard against misinterpretations of long-range correlation in a time series, and discuss relevance of this study to pattern recognition.
Unlike the well-studied models of growing networks, where the dominant dynamics consist of insertions of new nodes and connections, and rewiring of existing links, we study ad hoc networks, where one also has to contend with rapid and random deletions of existing nodes (and, hence, the associated links). We first show that dynamics based only on the well-known preferential attachments of new nodes do not lead to a sufficiently heavy-tailed degree distribution in ad hoc networks. In particular, the magnitude of the power-law exponent increases rapidly (from 3) with the deletion rate, becoming ∞ in the limit of equal insertion and deletion rates. We then introduce a local and universal compensatory rewiring dynamic, and show that even in the limit of equal insertion and deletion rates true scale-free structures emerge, where the degree distributions obey a power-law with a tunable exponent, which can be made arbitrarily close to -2. These results provide the first-known evidence of emergence of scale-free degree distributions purely due to dynamics, i.e., in networks of almost constant average size. The dynamics discovered in this paper can be used to craft protocols for designing highly dynamic Peer-to-Peer networks, and also to account for the power-law exponents observed in existing popular services.
We introduce a scalable searching protocol for locating and retrieving content in random networks with Power-Law (PL) and heavy-tailed degree distributions. The proposed algorithm is capable of finding any content in the network with probability one in time O(log N), with a total traffic that provably scales sub-linearly with the network size, N. Moreover, the protocol finds all contents reliably, even if every node in the network starts with a unique content. The scaling behavior of the size of the giant connected component of a random graph with heavy tailed degree distributions under bond percolation is at the heart of our results. The percolation search algorithm can be directly applied to make unstructured Peer-to-Peer (P2P) networks, such as Gnutella, Limewire and other file-sharing systems (which naturally display heavy-tailed degree distributions and scale-free network structures), scalable. For example, simulations of the protocol on the limewire crawl number 5 network[12], consisting of over 65,000 links and 10,000 nodes, show that even for such snapshot networks, the traffic can be reduced by a factor of at least 100, while achieving hit-rates greater than 90%.
We use sequential large-scale crawl data to empirically investigate and validate the dynamics that underlie the evolution of the structure of the web. We find that the overall structure of the web is defined by an intricate interplay between experience or entitlement of the pages (as measured by the number of inbound hyperlinks a page already has), inherent talent or fitness of the pages (as measured by the likelihood that someone visiting the page would give a hyperlink to it), and the continual high rates of birth and death of pages on the web. We find that the web is conservative in judging talent and the overall fitness distribution is exponential, showing low variability. The small variance in talent, however, is enough to lead to experience distributions with high variance: The preferential attachment mechanism amplifies these small biases and leads to heavy-tailed power-law (PL) inbound degree distributions over all pages, as well as over pages that are of the same age. The balancing act between experience and talent on the web allows newly introduced pages with novel and interesting content to grow quickly and surpass older pages. In this regard, it is much like what we observe in high-mobility and meritocratic societies: People with entitlement continue to have access to the best resources, but there is just enough screening for fitness that allows for talented winners to emerge and join the ranks of the leaders. Finally, we show that the fitness estimates have potential practical applications in ranking query results.A t both the individual and societal levels, we constantly have to make decisions on how we should distribute our limited resources and time. We need to choose who to hire, elect, buy from, get information from, award grants to, or make friends with. In this competitive landscape, each candidate touts a resumé highlighting experience, a more easily quantifiable metric that summarizes past achievements, e.g., the total number of clients a service provider has served or the years a prospective employee has spent at similar jobs, and talent or inherent fitness, a more subjective metric that indicates how well the candidates might perform in the future, e.g., special pedigree or degree from a prestigious college, or knowledge of a brand new technology, or an articulation of an ideal that captures the imagination. How we strike a balance between entitlement/experience and fitness/potential is a key determining factor in how wealth and power get distributed in a society and how nimble it is in adapting to changes. Too much emphasis on experience alone could lead to an ossified social structure that lacks innovation and can collapse dramatically when confronted with change; world history is littered with numerous instances of failed societies that had chosen such a path. The opposite extreme of letting only promising upstarts rule can equally easily lead to a state of anarchy with no dominant institutions to hold the society together; the frequent failures of well intentioned revolutions that sup...
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