1,748 research outputs found
Online Influence Maximization (Extended Version)
Social networks are commonly used for marketing purposes. For example, free
samples of a product can be given to a few influential social network users (or
"seed nodes"), with the hope that they will convince their friends to buy it.
One way to formalize marketers' objective is through influence maximization (or
IM), whose goal is to find the best seed nodes to activate under a fixed
budget, so that the number of people who get influenced in the end is
maximized. Recent solutions to IM rely on the influence probability that a user
influences another one. However, this probability information may be
unavailable or incomplete. In this paper, we study IM in the absence of
complete information on influence probability. We call this problem Online
Influence Maximization (OIM) since we learn influence probabilities at the same
time we run influence campaigns. To solve OIM, we propose a multiple-trial
approach, where (1) some seed nodes are selected based on existing influence
information; (2) an influence campaign is started with these seed nodes; and
(3) users' feedback is used to update influence information. We adopt the
Explore-Exploit strategy, which can select seed nodes using either the current
influence probability estimation (exploit), or the confidence bound on the
estimation (explore). Any existing IM algorithm can be used in this framework.
We also develop an incremental algorithm that can significantly reduce the
overhead of handling users' feedback information. Our experiments show that our
solution is more effective than traditional IM methods on the partial
information.Comment: 13 pages. To appear in KDD 2015. Extended versio
Segmentation of Fault Networks Determined from Spatial Clustering of Earthquakes
We present a new method of data clustering applied to earthquake catalogs,
with the goal of reconstructing the seismically active part of fault networks.
We first use an original method to separate clustered events from uncorrelated
seismicity using the distribution of volumes of tetrahedra defined by closest
neighbor events in the original and randomized seismic catalogs. The spatial
disorder of the complex geometry of fault networks is then taken into account
by defining faults as probabilistic anisotropic kernels, whose structures are
motivated by properties of discontinuous tectonic deformation and previous
empirical observations of the geometry of faults and of earthquake clusters at
many spatial and temporal scales. Combining this a priori knowledge with
information theoretical arguments, we propose the Gaussian mixture approach
implemented in an Expectation-Maximization (EM) procedure. A cross-validation
scheme is then used and allows the determination of the number of kernels that
should be used to provide an optimal data clustering of the catalog. This
three-steps approach is applied to a high quality relocated catalog of the
seismicity following the 1986 Mount Lewis () event in California and
reveals that events cluster along planar patches of about 2 km, i.e.
comparable to the size of the main event. The finite thickness of those
clusters (about 290 m) suggests that events do not occur on well-defined
euclidean fault core surfaces, but rather that the damage zone surrounding
faults may be seismically active at depth. Finally, we propose a connection
between our methodology and multi-scale spatial analysis, based on the
derivation of spatial fractal dimension of about 1.8 for the set of hypocenters
in the Mnt Lewis area, consistent with recent observations on relocated
catalogs
Who will lead and who will follow: Identifying Influential Users in Online Social Networks - A Critical Review and Future Research Directions
Along with the explosive growth of the phenomenon Online Social Networks (OSN), identifying influential users in OSN has received a great deal of attention in recent years. However, the development of practical approaches for identifying them is still in its infancy. By means of a structured literature review, the authors analyze and synthesize the publications particularly from two perspectives. From a research perspective, they find that existing approaches mostly build on users’ connectivity and activity but hardly consider further characteristics of influential users. Moreover, they outline two major research streams. It becomes apparent that most marketing-oriented articles draw on real-world data of OSN, while more technology-oriented papers rather have a theoretical approach and mostly evaluate their artifacts by means of formal proofs. The authors find that a stronger collaboration between the scientific Business and Information Systems Engineering (BISE) and Marketing communities could be mutually beneficial. With respect to a practitioner’s perspective, they compile advice on the practical application of approaches for the identification of influential users. It is hoped that the results can stimulate and guide future research
Shadowing and shielding: Effective heuristics for continuous influence maximisation in the voting dynamics
Influence maximisation, or how to affect the intrinsic opinion dynamics of a social group, is relevant for many applications, such as information campaigns, political competition, or marketing. Previous literature on influence maximisation has mostly explored discrete allocations of influence, i.e. optimally choosing a finite fixed number of nodes to target. Here, we study the generalised problem of continuous influence maximisation where nodes can be targeted with flexible intensity. We focus on optimal influence allocations against a passive opponent and compare the structure of the solutions in the continuous and discrete regimes. We find that, whereas hub allocations play a central role in explaining optimal allocations in the discrete regime, their explanatory power is strongly reduced in the continuous regime. Instead, we find that optimal continuous strategies are very well described by two other patterns: (i) targeting the same nodes as the opponent (shadowing) and (ii) targeting direct neighbours of the opponent (shielding). Finally, we investigate the game-theoretic scenario of two active opponents and show that the unique pure Nash equilibrium is to target all nodes equally. These results expose fundamental differences in the solutions to discrete and continuous regimes and provide novel effective heuristics for continuous influence maximisation
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