Given a network of interconnected nodes, each with its own value (such as a measurement, position, vote, or other data), we develop a distributed strategy that enables some or all of the nodes to calculate any arbitrary function of the node values, despite the actions of malicious nodes in the network. Our scheme assumes a broadcast model of communication (where all nodes transmit the same value to all of their neighbors) and utilizes a linear iteration where, at each time-step, each node updates its value to be a weighted average of its own previous value and those of its neighbors. We consider a node to be malicious or faulty if, instead of following the predefined linear strategy, it updates its value arbitrarily at each time-step (perhaps conspiring with other malicious nodes in the process). We show that the topology of the network completely characterizes the resilience of linear iterative strategies to this kind of malicious behavior. First, when the network contains or fewer vertex-disjoint paths from some node x(j) to another node x(i), we provide an explicit strategy for malicious nodes to follow in order to prevent node x(i) from receiving any information about x(j)'s value. Next, if node x(i) has at least 2f + 1 vertex-disjoint paths from every other (non-neighboring) node, we show that x(i) is guaranteed to be able to calculate any arbitrary function of all node values when the number of malicious nodes is or less. Furthermore, we show that this function can be calculated after running the linear iteration for a finite number of time-steps (upper bounded by the number of nodes in the network) with almost any set of weights (i.e., for all weights except for a set of measure zero).