Navigation in a small world

2008

Citations in the corpus

• Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs (2016, background): “, graphswith logarithmic or polylogarithmic scaling of the number of hops during the greedy traversal with the respect of the network size [31], [32].”
• The Length of Bridge Ties: Structural and Geographic Properties of Online Social Interactions (2012, background): “…and social factors and binds together individuals, affecting complex processes such as the spreading of information over social links (Rogers 1995; Newman, Barabasi, and Watts 2006) or the ability to navigate the social networks to route a message to a particular individual (Kleinberg 2000).”
• Complex systems: Network thinking (2006, unknown): “More recently, the computer scientist Jon Kleinberg proved that in networks following the Watts-Strogatz model, in which the random rewirings are chosen with uniform probability over all nodes, there is no e–cient decentralized search algorithm that will flnd short paths [31].”
• Reliability and Validity of the Personal Social Capital Scale 16 and Personal Social Capital Scale 8: Two Short Instruments for Survey Studies (2013, background): “Third, the approach does not consider the small world theory, which suggests that a person can find any person he or she wants in no more than six steps (Kleinberg 2000; Watts and Strogatz 1998).”
• The Economic Consequences of Social Network Structure (2017, unknown): “, Morris (2000)).”
• Navigable networks as Nash equilibria of navigation games (2014, result): “This is fully in line with the edge-locality principle of complex networks [68] [69] [70] which many times accounted for the high clustering coefficient.”
• VoroNet: A scalable object network based on Voronoi tessellations (unknown): “The function Choose-LRT is de(cid:12)ned by analogy to Kleinberg’s work [6].”
• Spatial Networks (2010, methodology): “In order to quantify this, Kleinberg [195] constructed a d-dimensional Watts-Strogatz model where each node i of the lattice”
• Topical interests and the mitigation of search engine bias (2005, background): “We will also refer to this quantity as the traffic to the same page.”
• "It's a small world after all": NoC performance optimization via long-range link insertion (2006, background): “In fact, many technological, biological, and social networks are neither completely regular nor completely irregular [22], [41], [42].”
• A Survey of Link Prediction in Social Networks (2011, background): “An evolution model predicts the future edges of a network, taking into account some well known attributes of social networks, such as the power law degree distribution [7] and the small world phenomenon [32].”
• HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning (2017, background): “Network data analysis and mining is an important research field because network data, capturing phenomena in various networks, such as social networks, paper citation networks, and World Wide Web, are ubiquitous in the real world [6, 15, 29].”
• VoroNet: A scalable object network based on Voronoi tessellations (2007, methodology): “Kleinberg proposed a small-world graph model [8, 9] that provides both poly-logarithmic paths between any two vertices (small-world property) and the navigability property: greedy decentralized path discovery algorithms can find poly-logarithmic paths in the graph between any couple of nodes.”
• Navigability of interconnected networks under random failures (2013, background): “It has been shown that the design and development of optimal navigation can be achieved exploiting shortest paths, either with local knowledge and the presence of long-range connections in a lattice network (25) or by imposing cost constraints, regardless of the available (global or local) informati”
• A state-space mixed membership blockmodel for dynamic network tomography (2008, background): “For example, researchers have found trends in a wide variety of large-scale networks, including scale-free and small-world properties [Barabasi and Albert (1999); Kleinberg (2000)].”
• Assessing the relevance of node features for network structure (2008, background): “[29]) and, in abstract terms, for navigability and efficiency [30, 31, 32].”
• Link prediction using supervised learning (2006, background): “Kleinberg [16, 21] discovered that in social network most of the nodes are connected with a very short distance.”