Scalable Mining of Large Graphs and Its Applications

In recent years, as the semantics of real-world applications becomes more and more complex, graph has emerged as a basic data manifestation unit in many domains, where its expressiveness can provide the best mathematical model for advanced use cases that involve a set of individual entities plus their inter-relationships. Responding to this trend, it is critically important to transform state-of-the-art data mining and data management systems so that they are able to handle such data sources with rich interconnecting structures, e.g., those representing XML, circuits, cyber-networks, chemical compounds, biological data, social data and the Web. One major challenge faced by the end-users of graph data is the large size of the graphs they want to analyze. Accompanying the arrival of the information era, huge repositories are created due to the constant flowing-in of data. People have been putting great emphasis on developing scalable methods to mine and manage large databases. Nonetheless, scalability is even more urgently expected in the graph scenario, since the structural complexity of graphs can often make computations more costly and thus the efficiency of algorithms is a critical concern. In this thesis, we are going to focus on scalable mining of large graphs. We propose a novel graph summarization technique to reduce the size of graphs, so that the computational complexity associated with processing the data is more manageable. In order to guarantee the result accuracy, we further relate patterns existing on the original graphs with those found on the summarized ones, and prove that they are very close to each other if certain conditions are met. This graph summarization concept has proved to be very useful for many concrete data analysis tasks, even including some cases where the underlying information is not naturally represented as graphs. For these cases, we build graphs to reveal insightful observations of the data from alternative angles, while summarization techniques are applied on the resulted graphs to make the analysis scalable. With efficient methods that are capable of extracting useful knowledge, one can also utilize the extracted knowledge to help other data management tasks on large graphs, as well. For the latter part of this thesis, we shall examine one application of scalable large graph mining algorithms, namely the indexing problem for graph search. We explore our research on both graph containment search and graph location search. In the first case, people care about a binary containment relationship between a query graph q and a target graph g. Focusing on the indexing deficiencies brought up by the large size of graphs, we design and implement a novel indexing scheme called CP-Index, which summarizes graphs for feature extraction and preserves the contact information among features for further pruning opportunities. In the second case where it becomes necessary to locate all identical copies of q in g given graphs with larger size and more complex internal structures, we take advantage of feature-based indexing in containment search to tackle the mining deficiency and pattern recurrence curse. A composite dynamic-and-static indexing (DS-Index) is further proposed, which is proved to be both effective and storage efficient

Franziska Struzek-Krähenbühl: Oszillation und Kristallisation. Theorie der Sprache bei Novalis

<p>A: Pooled sensitivity of WLI in the subgroup analysis. B: Pooled specificity of WLI in the subgroup analysis. C: Symmetric receiver operating characteristic (SROC) curve and area under the curve (AUC).</p

Diagnostic Efficacy of Magnifying Endoscopy with Narrow-Band Imaging for Gastric Neoplasms: A Meta-Analysis

<div><p>Background</p><p>Magnifying endoscopy with narrow-band imaging (ME-NBI) is a novel, image-enhanced endoscopic technique for differentiating gastrointestinal neoplasms and potentially enabling pathological diagnosis.</p><p>Objectives</p><p>The aim of this analysis was to assess the diagnostic performance of ME-NBI for gastric neoplasms.</p><p>Methods</p><p>We performed a systematic search of the PubMed, EMbase, Web of Science, and Cochrane Library databases for relevant studies. Meta-DiSc (version 1.4) and STATA (version 11.0) software were used for the data analysis. Random effects models were used to assess diagnostic efficacy. Heterogeneity was tested by the Q statistic and <i>I²</i> statistic. Meta-regression was used to analyze the sources of heterogeneity.</p><p>Results</p><p>A total of 10 studies, with 2151 lesions, were included. The pooled characteristics of these studies were as follows: sensitivity 0.85 (95% confidence interval [CI]: 0.81–0.89), specificity 0.96 (95% confidence interval [CI]: 0.95–0.97), and area under the curve (AUC) 0.9647. In the subgroup analysis, which compared the diagnostic efficacy of ME-NBI and white light imaging (WLI), the pooled sensitivity and specificity of ME-NBI were 0.87 (95% CI: 0.80–0.92) and 0.93 (95% CI: 0.90–0.95), respectively, and the area under the curve (AUC) was 0.9556. In contrast, the pooled sensitivity and specificity of WLI were 0.61 (95% CI: 0.53–0.69) and 0.65 (95% CI: 0.60–0.69), respectively, and the area under the curve (AUC) was 0.6772.</p><p>Conclusions</p><p>ME-NBI presents a high diagnostic value for gastric neoplasms and has a high specificity.</p></div

Flow diagram showing the selection process of articles.

<p>Flow diagram showing the selection process of articles.</p

Characteristics of the studies selected for the meta-analysis.

<p>NS, not stated; Yao et al, the classification system proposed by Yao et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123832#pone.0123832.ref029" target="_blank">29</a>]; Kaise et al, the classification system proposed by Kaise et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123832#pone.0123832.ref030" target="_blank">30</a>]; Original, the classification system proposed by the authors themselves; None, the study was conducted without following specific requirements.</p><p>Characteristics of the studies selected for the meta-analysis.</p

Quality assessment of the studies selected for the meta-analysis (QUADAS-2).

<p>L, low risk; H, high risk; U, unclear risk.</p><p>Quality assessment of the studies selected for the meta-analysis (QUADAS-2).</p

Results of the meta-regression performed to identify potential sources of heterogeneity.

<p>CI, confidence interval.</p><p>Results of the meta-regression performed to identify potential sources of heterogeneity.</p

O–O Radical Coupling: From Detailed Mechanistic Understanding to Enhanced Water Oxidation Catalysis

A deeper mechanistic understanding of the key O–O bond formation step of water oxidation by the [Ru­(bda)­(L)₂] (bdaH₂ = 2,2′-bipyridine-6,6′-dicarboxylic acid; L is a pyridine or isoquinoline derivative) family of catalysts is reached through harmonious experimental and computational studies of two series of modified catalysts with systematic variations in the axial ligands. The introduction of halogen and electron-donating substituents in [Ru­(bda)­(4-X-py)₂] and [Ru­(bda)­(6-X-isq)₂] (X is H, Cl, Br, and I for the pyridine series and H, F, Cl, Br, and OMe for the isoquinoline series) enhances the noncovalent interactions between the axial ligands in the transition state for the bimolecular O–O coupling, resulting in a lower activation barrier and faster catalysis. From detailed transition state calculations in combination with experimental kinetic studies, we find that the main contributor to the free energy of activation is entropy due to the highly organized transition states, which is contrary to other reports. Previous work has considered only the electronic influence of the substituents, suggesting electron-withdrawing groups accelerate catalysis, but we show that a balance between polarizability and favorable π–π interactions is the key, leading to rationally devised improvements. Our calculations predict the catalysts with the lowest Δ<i>G</i>^⧧ for the O–O coupling step to be [Ru­(bda)­(4-I-py)₂] and [Ru­(bda)­(6,7-(OMe)₂-isq)₂] for the pyridine and isoquinoline families, respectively. Our experimental results corroborate these predictions: the turnover frequency for [Ru­(bda)­(4-I-py)₂] (330 s^–1) is a 10-fold enhancement with respect to that of [Ru­(bda)­(py)₂], and the turnover frequency for [Ru­(bda)­(6-OMe-isq)₂] reaches 1270 s^–1, two times faster than [Ru­(bda)­(isq)₂]

Syntheses, structures, and luminescence of two Zn(II) coordination polymers based on 5-(4-imidazol-1-yl-phenyl)-2H-tetrazole and carboxylates

<div><p>Reactions of Zn(II) salts, 5-(4-(1H-imidazol-1-yl)phenyl)-1H-tetrazolate (HIPT) and 2-mercaptobenzoic acid or 2-propyl-1H-imidazole-4,5-dicarboxylic acid (H₃PrIDC), result in two mixed-ligand coordination polymers (CPs), [Zn₂(IPT)(DSDB)(OH)]_n (H₂DSDB = 2,2′-disulfanediyldibenzoic acid, <b>1</b>) and [Zn₂(IPT)(PrIDC)(H₂O)]_n (H₃PrIDC = 2-propyl-1H-imidazole-4,5-dicarboxylic acid, <b>2</b>). Compound <b>1</b> possesses a 2-D structure built by 1-D [Zn(IPT)]_n chains and DSDB²⁻ connectors, in which the DSDB²⁻ is generated <i>via in situ</i> reaction from 2-mercaptobenzoic acid. It displays a new intricate 4-nodal {3·4·6·7·8·9}{3·6·7·8·9·10}{3·8·9}{4·6·8} topology. Compound <b>2</b> displays a 3-D framework with new 3-connected topology with Schläfli symbol of (4·8·10) (8·12²), in which the 1-D Zn-carboxylate chains were bridged by 3-connected IPT⁻ ligands. The thermal stabilities and luminescence properties of <b>1</b> and <b>2</b> have also been studied. The compounds exhibit intense solid-state fluorescent emissions at room temperature.</p></div

Diagram illustrating the infection strategy, tetracycline clearing and microarray analysis of infected (S2) and uninfected (S2) cell lines

<p><b>Copyright information:</b></p><p>Taken from "Genome-wide analysis of the interaction between the endosymbiotic bacterium and its host"</p><p>BMC Genomics 2008;9():1-1.</p><p>Published online 2 Jan 2008</p><p>PMCID:PMC2253531.</p><p></p> The line illustrates an estimated infection level based upon periodic qPCR assays (indicated by hollow circles). The number provides a relative measure, not the absolute number of . The "waves" in the left half of the graph resulted from removal of the infection by host and enrichment of infection by shell vial introduction. Arrows indicate shell vial introduction of infection. At passage 17 (marked with asterisk), the cell line was split and one subline was tetracycline treated. At passage 23 (grey shading) the cell line was examined via microarray