arXiv subjects

Randall A. LaViolette

Publications and source records attributed to Randall A. LaViolette.

10 recordsLinked to original sources

arXiv2010

Sensitivity of the Performance of a Simple Exchange Model to its Topology

We study a simple exchange model in which price is fixed and the amount of a good transferred between actors depends only on the actors' respective budgets and the existence of a link between transacting actors. The model induces a simply-connected but possibly multi-component bipartite graph. A trading session on a fixed graph consists of a sequence of exchanges between connected buyers and sellers until no more exchanges are possible. We deem a trading session "feasible" if all of the buyers satisfy their respective demands. If all trading sessions are feasible the graph is deemed "successful", otherwise the feasibility of a trading session depends on the order of the sequence of exchanges. We demonstrate that topology is important for the success of trading sessions on graphs. In particular, for the case that supply equals demand for each component of the graph, we prove that the graph is successful if and only if the graph consists of components each of which are complete bipartite. For the case that supply exceeds demand, we prove that the other topologies also can be made successful but with finite reserve (i.e., excess supply) requirements that may grow proportional to the number of buyers. Finally, with computations for a small instance of the model, we provide an example of the wide range of performance in which only the connectivity varies. These results taken together place limits on the improvements in performance that can be expected from proposals to increase the connectivity of sparse exchange networks.

q-fin.TR↗

arXiv2009

Tolerating the Community Detection Resolution Limit with Edge Weighting

Communities of vertices within a giant network such as the World-Wide Web are likely to be vastly smaller than the network itself. However, Fortunato and Barthélemy have proved that modularity maximization algorithms for community detection may fail to resolve communities with fewer than $\sqrt{L/2}$ edges, where $L$ is the number of edges in the entire network. This resolution limit leads modularity maximization algorithms to have notoriously poor accuracy on many real networks. Fortunato and Barthélemy's argument can be extended to networks with weighted edges as well, and we derive this corollary argument. We conclude that weighted modularity algorithms may fail to resolve communities with fewer than $\sqrt{W ε/2}$ total edge weight, where $W$ is the total edge weight in the network and $ε$ is the maximum weight of an inter-community edge. If $ε$ is small, then small communities can be resolved. Given a weighted or unweighted network, we describe how to derive new edge weights in order to achieve a low $ε$, we modify the ``CNM'' community detection algorithm to maximize weighted modularity, and show that the resulting algorithm has greatly improved accuracy. In experiments with an emerging community standard benchmark, we find that our simple CNM variant is competitive with the most accurate community detection methods yet proposed.

physics.soc-ph↗

arXiv2009

Emergence of Price Divergence in a Model Short-Term Electric Power Market

A minimal model of a market of myopic non-cooperative agents who trade bilaterally with random bids reproduces qualitative features of short-term electric power markets, such as those in California and New England. Each agent knows its own budget and preferences but not those of any other agent. The near-equilibrium price established mid-way through the trading session diverges to both much higher and much lower prices towards the end of the trading session. This price divergence emerges in the model without any possibility that the agents could have conspired to "game" the market. The results were weakly sensitive to the endowments but strongly sensitive to the nature of the agent's preferences and budget constraints.

q-fin.TR↗

arXiv2007

Community Detection via Facility Location

In this paper we apply theoretical and practical results from facility location theory to the problem of community detection in networks. The result is an algorithm that computes bounds on a minimization variant of local modularity. We also define the concept of an edge support and a new measure of the goodness of community structures with respect to this concept. We present preliminary results and note that our methods are massively parallelizable.

physics.soc-ph↗

arXiv2007

Limits on Relief through Constrained Exchange on Random Graphs

Agents are represented by nodes on a random graph (e.g., small world or truncated power law). Each agent is endowed with a zero-mean random value that may be either positive or negative. All agents attempt to find relief, i.e., to reduce the magnitude of that initial value, to zero if possible, through exchanges. The exchange occurs only between agents that are linked, a constraint that turns out to dominate the results. The exchange process continues until a Pareto equilibrium is achieved. Only 40%-90% of the agents achieved relief on small world graphs with mean degree between 2 and 40. Even fewer agents achieved relief on scale-free like graphs with a truncated power law degree distribution. The rate at which relief grew with increasing degree was slow, only at most logarithmic for all of the graphs considered; viewed in reverse, relief is resilient to the removal of links.

physics.data-an↗

arXiv2005

The Impact of Social Networks on Multi-Agent Recommender Systems

Awerbuch et al.'s approach to distributed recommender systems (DRSs) is to have agents sample products at random while randomly querying one another for the best item they have found; we improve upon this by adding a communication network. Agents can only communicate with their immediate neighbors in the network, but neighboring agents may or may not represent users with common interests. We define two network structures: in the ``mailing-list model,'' agents representing similar users form cliques, while in the ``word-of-mouth model'' the agents are distributed randomly in a scale-free network (SFN). In both models, agents tell their neighbors about satisfactory products as they are found. In the word-of-mouth model, knowledge of items propagates only through interested agents, and the SFN parameters affect the system's performance. We include a summary of our new results on the character and parameters of random subgraphs of SFNs, in particular SFNs with power-law degree distributions down to minimum degree 1. These networks are not as resilient as Cohen et al. originally suggested. In the case of the widely-cited ``Internet resilience'' result, high failure rates actually lead to the orphaning of half of the surviving nodes after 60% of the network has failed and the complete disintegration of the network at 90%. We show that given an appropriate network, the communication network reduces the number of sampled items, the number of messages sent, and the amount of ``spam.'' We conclude that in many cases DRSs will be useful for sharing information in a multi-agent learning system.

cs.LG↗

arXiv2005

Parameters Affecting the Resilience of Scale-Free Networks to Random Failures

It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, the remaining network would continue to have a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions for practical purposes. In particular, we study finite scale-free networks which have minimum node degree of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to finite power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.

cs.NI↗

arXiv2004

Combining the ApEn statistic with surrogate data analysis for the detection of nonlinear dynamics in time series

We tested the natural combination of surrogate data analysis with the ApEn regularity statistic developed by Pincus [Proc. Natl. Acad. Sci. USA 88 (1991) 2297] by applying it to some popular models of nonlinear dynamics and publicly available experimental time series. We found that this easily implemented combination provided a useful method for discriminating signals governed by nonlinear dynamics from those governed by linear dynamics and noise. An apparently novel physical interpretation of ApEn also is supplied.

nlin.CD↗

arXiv2001

Quasi-chemical Theory for the Statistical Thermodynamics of the Hard Sphere Fluid

We develop a quasi-chemical theory for the study of packing thermodynamics in dense liquids. The situation of hard-core interactions is addressed by considering the binding of solvent molecules to a precisely defined `cavity' in order to assess the probability that the `cavity' is entirely evacuated. The primitive quasi-chemical approximation corresponds to a extension of the Poisson distribution used as a default model in an information theory approach. This primitive quasi-chemical theory is in good qualitative agreement with the observations for the hard sphere fluid of occupancy distributions that are central to quasi-chemical theories but begins to be quantitatively erroneous for the equation of state in the dense liquid regime of $ρd^3>$0.6. How the quasi-chemical approach can be iterated to treat correlation effects is addressed. Consideration of neglected correlation effects leads to a simple model for the form of those contributions neglected by the primitive quasi-chemical approximation. These considerations, supported by simulation observations, identify a `break away' phenomena that requires special thermodynamic consideration for the zero (0) occupancy case as distinct from the rest of the distribution. A empirical treatment leads to a one parameter model occupancy distribution that accurately fits the hard sphere equation of state and observed distributions.

physics.chem-ph↗

arXiv1998

Quasi-chemical Theories of Associated Liquids

It is shown how traditional development of theories of fluids based upon the concept of physical clustering can be adapted to an alternative local clustering definition. The alternative definition can preserve a detailed valence description of the interactions between a solution species and its near-neighbors, i.e., cooperativity and saturation of coordination for strong association. These clusters remain finite even for condensed phases. The simplest theory to which these developments lead is analogous to quasi-chemical theories of cooperative phenomena. The present quasi-chemical theories require additional consideration of packing issues because they don't impose lattice discretizations on the continuous problem. These quasi-chemical theories do not require pair decomposable interaction potential energy models. Since calculations may be required only for moderately sized clusters, we suggest that these quasi-chemical theories could be implemented with computational tools of current electronic structure theory. This can avoid an intermediate step of approximate force field generation.

physics.chem-ph↗