370 research outputs found
Mead and Husserl on the Self and Identification of the Subject
Out of many different strategies in the philosophy of the early 20th century the author compares
two completely different philosophies: G. H. Mead’s social behaviorism and E. Husserl’s transcendental
phenomenology with respect to the self-arising problem. For Mead, the initial point of his theory is
the social conduct or person’s behavior, whereas for Husserl, the life of the isolated transcendental
Ego is of greatest value. The author emphasizes, though the main ideas of both philosophers have
different methodological grounds, one finds, that the matter of primary importance for them. This is
the question of who is an executor of the social acts (Mead) and the transcendental phenomenological
act (Husserl)? Through an analysis of the main ideas of both philosophers (‘I’ and ‘Me’ as principles
of the subject by Mead, and intentionality, time analysis, and intersubjectivity by Husserl) the author
demonstrates, firstly, how the question of self-identity is solved in both conceptions, and, secondly,
how to argue the advantages of phenomenology. The article leads to the conclusion: methodologically
Mead’s social behaviorism is relativistic, as far as his theory of subjectivity depends on the social
context. Husserl’s method, despite its complexity, offers a clear subject structure and therefore can be
regarded as more productive for the theory of self. Refs 15
Compact Routing on Internet-Like Graphs
The Thorup-Zwick (TZ) routing scheme is the first generic stretch-3 routing
scheme delivering a nearly optimal local memory upper bound. Using both direct
analysis and simulation, we calculate the stretch distribution of this routing
scheme on random graphs with power-law node degree distributions, . We find that the average stretch is very low and virtually
independent of . In particular, for the Internet interdomain graph,
, the average stretch is around 1.1, with up to 70% of paths
being shortest. As the network grows, the average stretch slowly decreases. The
routing table is very small, too. It is well below its upper bounds, and its
size is around 50 records for -node networks. Furthermore, we find that
both the average shortest path length (i.e. distance) and width of
the distance distribution observed in the real Internet inter-AS graph
have values that are very close to the minimums of the average stretch in the
- and -directions. This leads us to the discovery of a unique
critical quasi-stationary point of the average TZ stretch as a function of
and . The Internet distance distribution is located in a
close neighborhood of this point. This observation suggests the analytical
structure of the average stretch function may be an indirect indicator of some
hidden optimization criteria influencing the Internet's interdomain topology
evolution.Comment: 29 pages, 16 figure
Network Geometry Inference using Common Neighbors
We introduce and explore a new method for inferring hidden geometric
coordinates of nodes in complex networks based on the number of common
neighbors between the nodes. We compare this approach to the HyperMap method,
which is based only on the connections (and disconnections) between the nodes,
i.e., on the links that the nodes have (or do not have). We find that for high
degree nodes the common-neighbors approach yields a more accurate inference
than the link-based method, unless heuristic periodic adjustments (or
"correction steps") are used in the latter. The common-neighbors approach is
computationally intensive, requiring running time to map a network of
nodes, versus in the link-based method. But we also develop a
hybrid method with running time, which combines the common-neighbors
and link-based approaches, and explore a heuristic that reduces its running
time further to , without significant reduction in the mapping
accuracy. We apply this method to the Autonomous Systems (AS) Internet, and
reveal how soft communities of ASes evolve over time in the similarity space.
We further demonstrate the method's predictive power by forecasting future
links between ASes. Taken altogether, our results advance our understanding of
how to efficiently and accurately map real networks to their latent geometric
spaces, which is an important necessary step towards understanding the laws
that govern the dynamics of nodes in these spaces, and the fine-grained
dynamics of network connections
Sparse Maximum-Entropy Random Graphs with a Given Power-Law Degree Distribution
Even though power-law or close-to-power-law degree distributions are
ubiquitously observed in a great variety of large real networks, the
mathematically satisfactory treatment of random power-law graphs satisfying
basic statistical requirements of realism is still lacking. These requirements
are: sparsity, exchangeability, projectivity, and unbiasedness. The last
requirement states that entropy of the graph ensemble must be maximized under
the degree distribution constraints. Here we prove that the hypersoft
configuration model (HSCM), belonging to the class of random graphs with latent
hyperparameters, also known as inhomogeneous random graphs or -random
graphs, is an ensemble of random power-law graphs that are sparse, unbiased,
and either exchangeable or projective. The proof of their unbiasedness relies
on generalized graphons, and on mapping the problem of maximization of the
normalized Gibbs entropy of a random graph ensemble, to the graphon entropy
maximization problem, showing that the two entropies converge to each other in
the large-graph limit
Percolation in self-similar networks
We provide a simple proof that graphs in a general class of self-similar
networks have zero percolation threshold. The considered self-similar networks
include random scale-free graphs with given expected node degrees and zero
clustering, scale-free graphs with finite clustering and metric structure,
growing scale-free networks, and many real networks. The proof and the
derivation of the giant component size do not require the assumption that
networks are treelike. Our results rely only on the observation that
self-similar networks possess a hierarchy of nested subgraphs whose average
degree grows with their depth in the hierarchy. We conjecture that this
property is pivotal for percolation in networks.Comment: 4 pages, 3 figure
Greedy Forwarding in Dynamic Scale-Free Networks Embedded in Hyperbolic Metric Spaces
We show that complex (scale-free) network topologies naturally emerge from
hyperbolic metric spaces. Hyperbolic geometry facilitates maximally efficient
greedy forwarding in these networks. Greedy forwarding is topology-oblivious.
Nevertheless, greedy packets find their destinations with 100% probability
following almost optimal shortest paths. This remarkable efficiency sustains
even in highly dynamic networks. Our findings suggest that forwarding
information through complex networks, such as the Internet, is possible without
the overhead of existing routing protocols, and may also find practical
applications in overlay networks for tasks such as application-level routing,
information sharing, and data distribution
- …