Abstract:
In a "tipping" model, each node in a social network, representing an individual, adopts a behavior if a certain number of his incoming neighbors previously held that property. A key problem for viral marketers is to determine an initial "seed" set in a network such that if given a property then the entire network adopts the behavior. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds such sets that are several orders of magnitude smaller than the population size. Our approach also scales well - on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed sets in under 3.6 hours. We also find that highly clustered local neighborhoods and dense network-wide community structure together suppress the ability of a trend to spread under the tipping model.

Abstract:
We hear it all too often in the media: an organization is attacked, its data, often containing personally identifying information, is made public, and a hacking group emerges to claim credit. In this excerpt, we discuss how such groups operate and describe the details of a few major cyber-attacks of this sort in the wider context of how they occurred. We feel that understanding how such groups have operated in the past will give organizations ideas of how to defend against them in the future.

Abstract:
Along with the USA and Russia, China is often considered one of the leading cyber-powers in the world. In this excerpt, we explore how Chinese military thought, developed in the 1990s, influenced their cyber-operations in the early 2000s. In particular, we examine the ideas of "Unrestricted Warfare" and "Active Offense" and discuss how they can permit for the theft of intellectual property. We then specifically look at how the case study of Operation Aurora, a cyber-operation directed against many major U.S. technology and defense firms, reflects some of these ideas.

Abstract:
We study the behavior of pathogens on host protein networks for humans and Arabidopsis - noting striking similarities. Specifically, we preform -shell decomposition analysis on these networks - which groups the proteins into various “shells” based on network structure. We observe that shells with a higher average degree are more highly targeted (with a power-law relationship) and that highly targeted nodes lie in shells closer to the inner-core of the network. Additionally, we also note that the inner core of the network is significantly under-targeted. We show that these core proteins may have a role in intra-cellular communication and hypothesize that they are less attacked to ensure survival of the host. This may explain why certain high-degree proteins are not significantly attacked.

Abstract:
There are numerous applications which require the ability to take certain actions (e.g. distribute money, medicines, people etc.) over a geographic region. A disaster relief organization must allocate people and supplies to parts of a region after a disaster. A public health organization must allocate limited vaccine to people across a region. In both cases, the organization is trying to optimize something (e.g. minimize expected number of people with a disease). We introduce "geospatial optimization problems" (GOPs) where an organization has limited resources and budget to take actions in a geographic area. The actions result in one or more properties changing for one or more locations. There are also certain constraints on the combinations of actions that can be taken. We study two types of GOPs - goal-based and benefit-maximizing (GBGOP and BMGOP respectively). A GBGOP ensures that certain properties must be true at specified locations after the actions are taken while a BMGOP optimizes a linear benefit function. We show both problems to be NP-hard (with membership in NP for the associated decision problems). Additionally, we prove limits on approximation for both problems. We present integer programs for both GOPs that provide exact solutions. We also correctly reduce the number of variables in for the GBGOP integer constraints. For BMGOP, we present the BMGOP-Compute algorithm that runs in PTIME and provides a reasonable approximation guarantee in most cases.

Abstract:
In a "tipping" model, each node in a social network, representing an individual, adopts a property or behavior if a certain number of his incoming neighbors currently exhibit the same. In viral marketing, a key problem is to select an initial "seed" set from the network such that the entire network adopts any behavior given to the seed. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds seed sets that are several orders of magnitude smaller than the population size and outperform nodal centrality measures in most cases. In addition, our approach scales well - on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed set in under 3.6 hours. Our experiments also indicate that our algorithm provides small seed sets even if high-degree nodes are removed. Lastly, we find that highly clustered local neighborhoods, together with dense network-wide community structures, suppress a trend's ability to spread under the tipping model.

Abstract:
The modeling of cascade processes in multi-agent systems in the form of complex networks has in recent years become an important topic of study due to its many applications: the adoption of commercial products, spread of disease, the diffusion of an idea, etc. In this paper, we begin by identifying a desiderata of seven properties that a framework for modeling such processes should satisfy: the ability to represent attributes of both nodes and edges, an explicit representation of time, the ability to represent non-Markovian temporal relationships, representation of uncertain information, the ability to represent competing cascades, allowance of non-monotonic diffusion, and computational tractability. We then present the MANCaLog language, a formalism based on logic programming that satisfies all these desiderata, and focus on algorithms for finding minimal models (from which the outcome of cascades can be obtained) as well as how this formalism can be applied in real world scenarios. We are not aware of any other formalism in the literature that meets all of the above requirements.

Abstract:
Evolutionary graph theory studies the evolutionary dynamics of populations structured on graphs. A central problem is determining the probability that a small number of mutants overtake a population. Currently, Monte Carlo simulations are used for estimating such fixation probabilities on general directed graphs, since no good analytical methods exist. In this paper, we introduce a novel deterministic framework for computing fixation probabilities for strongly connected, directed, weighted evolutionary graphs under neutral drift. We show how this framework can also be used to calculate the expected number of mutants at a given time step (even if we relax the assumption that the graph is strongly connected), how it can extend to other related models (e.g. voter model), how our framework can provide non-trivial bounds for fixation probability in the case of an advantageous mutant, and how it can be used to find a non-trivial lower bound on the mean time to fixation. We provide various experimental results determining fixation probabilities and expected number of mutants on different graphs. Among these, we show that our method consistently outperforms Monte Carlo simulations in speed by several orders of magnitude. Finally we show how our approach can provide insight into synaptic competition in neurology.

Abstract:
An adversary looking to disrupt a power grid may look to target certain substations and sources of power generation to initiate a cascading failure that maximizes the number of customers without electricity. This is particularly an important concern when the enemy has the capability to launch cyber-attacks as practical concerns (i.e. avoiding disruption of service, presence of legacy systems, etc.) may hinder security. Hence, a defender can harden the security posture at certain power stations but may lack the time and resources to do this for the entire power grid. We model a power grid as a graph and introduce the cascading failure game in which both the defender and attacker choose a subset of power stations such as to minimize (maximize) the number of consumers having access to producers of power. We formalize problems for identifying both mixed and deterministic strategies for both players, prove complexity results under a variety of different scenarios, identify tractable cases, and develop algorithms for these problems. We also perform an experimental evaluation of the model and game on a real-world power grid network. Empirically, we noted that the game favors the attacker as he benefits more from increased resources than the defender. Further, the minimax defense produces roughly the same expected payoff as an easy-to-compute deterministic load based (DLB) defense when played against a minimax attack strategy. However, DLB performs more poorly than minimax defense when faced with the attacker's best response to DLB. This is likely due to the presence of low-load yet high-payoff nodes, which we also found in our empirical analysis.

Abstract:
In this paper we introduce the Organization, Relationship, and Contact Analyzer (ORCA) that is designed to aide intelligence analysis for law enforcement operations against violent street gangs. ORCA is designed to address several police analytical needs concerning street gangs using new techniques in social network analysis. Specifically, it can determine "degree of membership" for individuals who do not admit to membership in a street gang, quickly identify sets of influential individuals (under the tipping model), and identify criminal ecosystems by decomposing gangs into sub-groups. We describe this software and the design decisions considered in building an intelligence analysis tool created specifically for countering violent street gangs as well as provide results based on conducting analysis on real-world police data provided by a major American metropolitan police department who is partnering with us and currently deploying this system for real-world use.