The rich-club phenomenon across complex network hierarchies

The so-called rich-club phenomenon in a complex network is characterized when nodes of higher degree (hubs) are better connected among themselves than are nodes with smaller degree. The presence of the rich-club phenomenon may be an indicator of several interesting high-level network properties, such as tolerance to hub failures. Here we investigate the existence of the rich-club phenomenon across the hierarchical degrees of a number of real-world networks. Our simulations reveal that the phenomenon may appear in some hierarchies but not in others and, moreover, that it may appear and disappear as we move across hierarchies. This reveals the interesting possibility of non-monotonic behavior of the phenomenon; the possible implications of our findings are discussed.Comment: 4 page

Sinorhizobium Meliloti, A Bacterium Lacking The Autoinducer-2 (AI-2) Synthase, Responds To AI-2 Supplied By Other Bacteria

Many bacterial species respond to the quorum-sensing signal autoinducer-2 (AI-2) by regulating different niche-specific genes. Here, we show that Sinorhizobium meliloti, a plant symbiont lacking the gene for the AI-2 synthase, while not capable of producing AI-2 can nonetheless respond to AI-2 produced by other species. We demonstrate that S. meliloti has a periplasmic binding protein that binds AI-2. The crystal structure of this protein (here named SmlsrB) with its ligand reveals that it binds (2R,4S)-2-methyl-2,3,3,4-tetrahydroxytetrahydrofuran (R-THMF), the identical AI-2 isomer recognized by LsrB of Salmonella typhimurium. The gene encoding SmlsrB is in an operon with orthologues of the lsr genes required for AI-2 internalization in enteric bacteria. Accordingly, S. meliloti internalizes exogenous AI-2, and mutants in this operon are defective in AI-2 internalization. S. meliloti does not gain a metabolic benefit from internalizing AI-2, suggesting that AI-2 functions as a signal in S. meliloti. Furthermore, S. meliloti can completely eliminate the AI-2 secreted by Erwinia carotovora, a plant pathogen shown to use AI-2 to regulate virulence. Our findings suggest that S. meliloti is capable of \u27eavesdropping\u27 on the AI-2 signalling of other species and interfering with AI-2-regulated behaviours such as virulence

Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook

A crucial task in the analysis of on-line social-networking systems is to identify important people --- those linked by strong social ties --- within an individual's network neighborhood. Here we investigate this question for a particular category of strong ties, those involving spouses or romantic partners. We organize our analysis around a basic question: given all the connections among a person's friends, can you recognize his or her romantic partner from the network structure alone? Using data from a large sample of Facebook users, we find that this task can be accomplished with high accuracy, but doing so requires the development of a new measure of tie strength that we term `dispersion' --- the extent to which two people's mutual friends are not themselves well-connected. The results offer methods for identifying types of structurally significant people in on-line applications, and suggest a potential expansion of existing theories of tie strength.Comment: Proc. 17th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW), 201

Living with the user: Design drama for dementia care through responsive scripted experiences in the home

Participation in forms of drama and narrative can provoke empathy and creativity in user-centred design processes. In this paper, we expand upon existing methods to explore the potential for responsive scripted experiences that are delivered through the combination of sensors and output devices placed in a home. The approach is being developed in the context of Dementia care, where the capacity for rich user participation in design activities is limited. In this case, a system can act as a proxy for a person with Dementia, allowing designers to gain experiences and insight as to what it is like to provide care for, and live with, this person. We describe the rationale behind the approach, a prototype system architecture, and our current work to explore the creation of scripted experiences for design, played out though UbiComp technologies.This research is funded by the Arts and Humanities Research Council UK, (AH/K00266X/1) and Horizon Digital Economy Research (RCUK grant EP/G065802/1)

Expulsion of Symbiotic Algae during Feeding by the Green Hydra – a Mechanism for Regulating Symbiont Density?

Background: Algal-cnidarian symbiosis is one of the main factors contributing to the success of cnidarians, and is crucial for the maintenance of coral reefs. While loss of the symbionts (such as in coral bleaching) may cause the death of the cnidarian host, over-proliferation of the algae may also harm the host. Thus, there is a need for the host to regulate the population density of its symbionts. In the green hydra, Chlorohydra viridissima, the density of symbiotic algae may be controlled through host modulation of the algal cell cycle. Alternatively, Chlorohydra may actively expel their endosymbionts, although this phenomenon has only been observed under experimentally contrived stress conditions. Principal Findings: We show, using light and electron microscopy, that Chlorohydra actively expel endosymbiotic algal cells during predatory feeding on Artemia. This expulsion occurs as part of the apocrine mode of secretion from the endodermal digestive cells, but may also occur via an independent exocytotic mechanism. Significance: Our results demonstrate, for the first time, active expulsion of endosymbiotic algae from cnidarians under natural conditions. We suggest this phenomenon may represent a mechanism whereby cnidarians can expel excess symbiotic algae when an alternative form of nutrition is available in the form of prey

Empirical evaluation of different feature representations for social circles detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_4Social circles detection is a special case of community detection in social network that is currently attracting a growing interest in the research community. We propose in this paper an empirical evaluation of the multi-assignment clustering method using different feature representation models. We define different vectorial representations from both structural egonet information and user profile features. We study and compare the performance on the available labelled Facebook data from the Kaggle competition on learning social circles in networks. We compare our results with several different baselines.This work was developed in the framework of the W911NF-14-1-0254 research project Social Copying Community Detection (SOCOCODE), fundedby the US Army Research Office (ARO).Alonso, J.; Paredes Palacios, R.; Rosso, P. (2015). Empirical evaluation of different feature representations for social circles detection. En Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings. Springer International Publishing. 31-38. https://doi.org/10.1007/978-3-319-19390-8_4S3138Buhmann, J., Kühnel, H.: Vector quantization with complexity costs. IEEE Trans. Inf. Theor. 39(4), 1133–1145 (1993)Dey, K., Bandyopadhyay, S.: An empirical investigation of like-mindedness of topically related social communities on microblogging platforms. In: International Conference on Natural Languages (2013)Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)Frank, M., Streich, A.P., Basin, D., Buhmann, J.M.: Multi-assignment clustering for boolean data. J. Mach. Learn. Res. 13(1), 459–489 (2012)Kaggle: Learning social circles in networks. http://www.kaggle.com/c/learning-social-circlesMcAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. Adv. Neural Inf. Process. Syst. 25, 539–547 (2012)McAuley, J., Leskovec, J.: Discovering social circles in ego networks. ACM Trans. Knowl. Discov. Data (TKDD) 8(1), 4 (2014)Palla, G., Dernyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)Pathak, N., DeLong, C., Banerjee, A., Erickson, K.: Social topic models for community extraction. In: The 2nd SNA-KDD Workshop (2008)Porter, M.A., Onnela, J.P., Mucha, P.J.: Communities in networks. Not. Amer. Math. Soc. 56(9), 1082–1097 (2009)Rose, K., Gurewitz, E., Fox, G.C.: Vector quantization by deterministic annealing. IEEE Transactions on Information Theory 38(4), 1249–1257 (1992)Sachan, M., Contractor, D., Faruquie, T.A., Subramaniam, L.V.: Using content and interactions for discovering communities in social networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 331–340 (2012)Streich, A.P., Frank, M., Basin, D., Buhmann, J.M.: Multi-assignment clustering for Boolean data. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 969–976 (2009)Vaidya, J., Atluri, V., Guo, Q.: The role mining problem: finding a minimal descriptive set of roles. In: Proceedings of the 12th ACM Symposium on Access Control Models and Technologies, pp. 175–184 (2007)Zhou, D., Councill, I., Zha, H., Giles, C.L.: Discovering temporal communities from social network documents. In: Seventh IEEE International Conference on Data Mining, PP. 745–750 (2007