In this paper we study how to discover the evolution of topics over time in a time-stamped document collection. Our approach is uniquely designed to capture the rich topology of topic evolution inherent in the corpus. Instead of characterizing the evolving topics at fixed time points, we conceptually define a topic as a quantized unit of evolutionary change in content and discover topics with the time of their appearance in the corpus. Discovered topics are then connected to form a topic evolution graph using a measure derived from the underlying document network. Our approach allows inhomogeneous distribution of topics over time and does not impose any topological restriction in topic evolution graphs. We evaluate our algorithm on the ACM corpus.

We present, and evaluate benefits of, a design methodology for translating natural phenomena represented as mathematical models, into novel, self-adaptive, peer-to-peer (p2p) distributed computing algorithms ( protocols ). Concretely, our first contribution is a set of techniques to translate discrete sequence equations (also known as difference equations) into new p2p protocols called sequence protocols . Sequence protocols are self-adaptive, scalable, and fault-tolerant, with applicability in p2p settings like Grids. A sequence protocol is a set of probabilistic local and message-passing actions for each process. These actions are translated from terms in a set of source sequence equations. Individual processes do not simulate the source sequence equations completely. Instead, each process executes probabilistic local and message passing actions, so that the emergent round-to-round behavior of the sequence protocol in a p2p system can be probabilistically predicted by the source sequence equations. The article's second contribution is the design and evaluation of a set of sequence protocols for detection of two global triggers in a distributed system: threshold detection and interval detection. This article's third contribution is a new self-adaptive Grid computing protocol called HoneyAdapt. HoneyAdapt is derived from sequence equations modeling adaptive bee foraging behavior in nature. HoneyAdapt is intended for Grid applications that allow Grid clients, at run-time, a choice of algorithms for executing chunks of the application's dataset. HoneyAdapt tells each Grid client how to adaptively select at run-time, for each chunk it receives, a good algorithm for computing the chunk—this selection is based on continuous feedback from other clients. Finally, we design a variant of HoneyAdapt, called HoneySort, for application to Grid parallelized sorting settings using the master-worker paradigm. Our evaluation of these contributions consists of mathematical analysis, large-scale trace-based simulation results, and experimental results from a HoneySort deployment.

In this paper we address the problem of detecting topics in large-scale linked document collections. Recently, topic detection has become a very active area of research due to its utility for information navigation, trend analysis, and high-level description of data. We present a unique approach that uses the correlation between the distribution of a term that represents a topic and the link distribution in the citation graph where the nodes are limited to the documents containing the term. This tight coupling between term and graph analysis is distinguished from other approaches such as those that focus on language models. We develop a topic score measure for each term, using the likelihood ratio of binary hypotheses based on a probabilistic description of graph connectivity. Our approach is based on the intuition that if a term is relevant to a topic, the documents containing the term have denser connectivity than a random selection of documents. We extend our algorithm to detect a topic represented by a set of terms, using the intuition that if the co-occurrence of terms represents a new topic, the citation pattern should exhibit the synergistic effect. We test our algorithm on two electronic research literature collections,arXiv and Citeseer.Our evaluation shows that the approach is effective and reveals some novel aspects of topic detection.